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ISEBSWTINT-001 ISEB Software Testing Intermediate

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ISEBSWTINT-001 exam Dumps Source : ISEB Software Testing Intermediate

Test Code : ISEBSWTINT-001
Test denomination : ISEB Software Testing Intermediate
Vendor denomination : ISEB
: 25 actual Questions

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ISEB ISEB Software Testing Intermediate

TietoEnator Certifies Testers | killexams.com actual Questions and Pass4sure dumps

January 14, 2002 08:28 ET | supply: TietoEnator

ESPOO, Finland, Jan. 14, 2002 (PRIMEZONE) -- TietoEnator is one among two Swedish companies authorised to certify testers in response to the ISEB groundwork certificates for application testing. The ISEB test practising might live offered in Sweden and Norway from January.

TietoEnator has its personal peruse at various academics and offers the route to purchasers and personnel. it is a 3-day direction, and on the conclusion of day three the participants can pick to enmesh an exam and glean the ISEB-certification.

- they fill noticed an increasing require for licensed testers, and on the grounds that there isn't any Swedish accustomed for check, we've chosen to give the ISEB groundwork certificates, says Thomas Klarbrant, Managing Director of TietoEnator test solutions.

ISEB (advice methods Examination Board) is a division inside BCS (British laptop Society). ISEB presents certifications within several distinctive IT areas. The purpose of ISEB is to carry the specifications in the IT industry and to aid competence development.

For additional suggestions, gladden contact: Kennet Osbjer, TietoEnator examine solutions, Sweden, +forty six 706 24 65 33 Marit Saelemyr, TietoEnator Consulting AS, Norway, +47 553 64468

With over 10,000 personnel and annual web income of EUR 1.1 billion, TietoEnator is a number one agency of excessive price-introduced IT features in Europe. TietoEnator specializes in consulting, constructing and internet hosting its purchasers' enterprise operations in the digital economy. The group's functions are according to a coalesce of deep business-specific talents and newest suggestions know-how. www.tietoenator.com

TIETOENATOR industry enterprise

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TietoEnator, Espoo TietoEnator test options, Sweden: Kennet Osbjer +46 706 24 sixty five 33 TietoEnator Consulting AS, Norwa: Marit Saelemyr +47 553 64468

Espoo, FINLAND

TietoEnator, Espoo TietoEnator verify options, Sweden: Kennet Osbjer +46 706 24 sixty five 33 TietoEnator Consulting AS, Norwa: Marit Saelemyr +forty seven 553 64468

Sogeti neighborhood Acquires Vizuri and Expands UK Operations in testing Market | killexams.com actual Questions and Pass4sure dumps

LONDON & PARIS--(enterprise WIRE)--Sogeti, probably the most world's highest attribute suppliers of indigenous IT, skilled and test capabilities, nowadays introduced the acquisition of the uk peruse at various consultancy Vizuri restrained.

This acquisition, at the side of the acquisition of perception test capabilities in April 2008 and other recruitment plans, will sequel in Sogeti UK starting to live to 250 workforce via the conclusion of 2009, organising its position as an colossal player in the UK testing capabilities Market.

Vizuri is one of the UK’s leading specialist application exceptional Assurance, verify, and security Consultancy service providers. With a corpse of workers of 90 individuals, the uk industry offers Consulting, Managed capabilities, practicing and Resourcing capabilities in these particular areas to over 50 purchasers. relish Sogeti, the enterprise has a local fork industry model providing quick, flexible, lofty expense capabilities to shoppers across the uk.

Sogeti, a member of the Capgemini neighborhood, is a world chief in the provision of local IT skilled services and employs in extra of 20,000 people supplying features in excessive Tech Consulting, utility functions / programs Integration and Infrastructure functions. Globally, Sogeti is the market leader in application QA & verify functions with in extra of two,500 testing experts of which 2,000 in Europe and country and 500 individuals in India, which represents a tenacious offshore funding.

in addition, there are 5,000 software experts with apropos check experiences. Sogeti is the owner, developer and distributor of the world main peruse at various methods TMAP® (verify administration approach) and TPI® (examine system growth). Sogeti offers titanic value in onshore-and offshore features within the testing enviornment.

“This acquisition reinforces the european management of Sogeti in IT local functions by route of consolidating its activities in one of the fundamental markets - the United Kingdom”, talked about Luc-François Salvador, Chairman of the Sogeti neighborhood. introduced Richard Terry, Managing Director of Sogeti UK: “We conform with that through the acquisition of Vizuri, they can automatically live in a position to give a broader, deeper compass of capabilities to every separate of their customers. further, the mixed industry enterprise offers us the crucial mass to speed up the growth of the enterprise to achieve a market leadership position. this will earn us greater eye-catching to their current and future shoppers and as an organisation.” Brian Shea, CEO of Vizuri believes that: “When combining the shoppers and team of workers of Vizuri with the energy of Sogeti they can create an organization with the intent to deliver proper creative market management within the UK. they are able to leverage the strengths of both to deliver many merits to shoppers and group of workers. here's a extremely unique time in their market and this fresh entity can live uniquely placed to carry actual client profit for years to return.”

As illustrated through this deal, Capgemini neighborhood - via Sogeti in this case -continues to expand chiefly through focused and disciplined acquisitions of firms with robust entrepreneurial spirit; locality or technological advantage; and client intimacy.

About Sogeti testing

Sogeti is a global leader in imaginitive, business-pushed powerful assurance and testing services with resourceful options: Managed trying out services, TMap®-based challenge and software testing, and test procedure development. because the first testing provider company in Europe and u . s . a ., with over 2,500 test professionals and a further 5,000 utility consultants with peruse at various event, in 14 countries global, they aid businesses obtain their checking out and QA desires.

About Vizuri

Vizuri is an unbiased company of IT (risk management, checking out, security and practising capabilities). primarily based in London, Vizuri has scholarship in providing options for govt, fresh Media, Gaming, Utilities, pecuniary features and assurance organizations. Vizuri strives for delivery excellence and habitual growth for both customers and it’s personal company and personnel. As an ISO9001 approved business, Vizuri are required to invariably solicit client comments. each Vizuri consultant is formally informed (all are ISEB certified) and developed according to Vizuri’s Investor In people (IIP) commonplace.

Vizuri is authorised via UKAS for the delivery of CSIA Claims established trail (CCTM) laboratory testing functions and is approved by the united kingdom gambling commission for the trying out of far flung gaming techniques. QBIT, a completely owned Vizuri subsidiary gives a comprehensive compass of theoretical and practical courses including ISEB foundation, Intermediate & Practitioner classes in software testing. QBIT is an Institute Of IT practicing authorised working towards company.

For extra tips, gladden contemplate www.vizuri.co.uk and www.qbit.co.uk.

About Sogeti UK

Sogeti UK is a premier issuer of software checking out and infrastructure features to corporate and public-sector purchasers. Their collaborative fashion is grounded in proprietary and innovative methodologies that manage risk and produce identifiable results. Sogeti's group of skilled IT professionals works intently with consumers to design, ameliorate and installation adaptable solutions that tackle the spicy needs of their industry and industry. Sogeti grants functional capabilities, built on greater than 40 years of world event.

For greater counsel, gladden consult with www.uk.sogeti.com.

ABOUT SOGETI group

Sogeti neighborhood (member of the Capgemini group), leader in IT capabilities and R&D outsourcing, presents more than a few local professional features in lofty Tech Consulting and counsel know-how for giant agencies through these three complementary areas:

- excessive Tech or lofty Tech Consulting

analysis & evolution with the preservation in operational situations:

Outsourcing research and construction and Innovation assistance.

Scientific and technical analysis and trends, mechanical design, construction of complicated methods.

control and industrial records processing.

- software services or programs integration

From the theory to the upkeep of the suggestions equipment: consulting, structure, contracting owner’s suggestions, building, integration, testing and preservation of application belongings.

- Infrastructure features or integration administration and programs administration

Consulting in security and performance of the infrastructures of functions IT and Telecom

provide of every separate of the capabilities integral to the explanation, to the optimization and to the management of the IT buildings. Integration of the architectures and of the exploitation, guaranteeing an foremost effectivity of the programs and networks.

In total, Sogeti neighborhood has greater than 20,000 professionals in 14 nations with 10,000 in France.

For more suggestions: www.sogeti.com


Quexst Brings assistance systems route To India | killexams.com actual Questions and Pass4sure dumps

information systems Examination Board (ISEB), a universally identified qualification provided via the British laptop Society (BCS), hitherto may simplest live got with the aid of in fact giving the examination in the UK, can now live given sitting prerogative here in India. This has been made viable due to the alliance cast through Pune-primarily based Quexst friends with UK-primarily based Unicom Seminars Ltd.

talking to CXOtoday, Vandana Dandekar, joint chief executive officer of Quexst, pointed out, “ISEB is a separate certificate path touching every separate main areas of advice methods (IS) including administration, building, provider start, and pleasant.”

in accordance with Dandekar, corporates relish Satyam fill their employees licensed the entire means from UK. however, with this alliance, Quexst turns into the primary enterprise to convey the ISEB practicing in India. The BCS in affiliation with Unicom would deliver the classes in India.

Quexst offers exhaustive practicing that customarily stretches between three to five days. The examination is supervised by means of the laptop Society of India, whereas the papers are corrected through the BCS. The certification too is awarded from BCS.

in terms of eligibility, aside from the obligatory commencement, the plane of content is such that it necessitates fingers-on software evolution sustain of two to a few years.

charge-clever she mentioned it is comparatively less costly than what is offered in UK. Disclosed Dandekar, “The path expense tiers from Rs 15,000 to Rs forty,000, whereas the examination fees may well live between Rs 5000-7000.”

So, who are their goal viewers?

Says Dandekar, “In view of the steep prices of the classes, they are expecting corporate sponsorships for personnel who would relish to enmesh in the course. additionally, when builders are ISEB-licensed, IT companies can derive better mileage in terms of positioning their products or services within the world markets, principally within the European market.”

under the latest settlement with Unicom, Quexst will present the classes in Delhi, Calcutta, Pune and Mumbai. meanwhile in the Southern belt, they intent to present it in two facilities from the three zeroed every separate the route down to specifically, Hyderabad, Chennai, and Bangalore. Six months later, they intent to present it in cities akin to Nagpur, Indore due to the fact that these are feeder towns to the IT business.

Quexst acquaintances offers features in software testing, process consulting and practising capabilities.


ISEBSWTINT-001 ISEB Software Testing Intermediate

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ISEBSWTINT-001 exam Dumps Source : ISEB Software Testing Intermediate

Test Code : ISEBSWTINT-001
Test denomination : ISEB Software Testing Intermediate
Vendor denomination : ISEB
: 25 actual Questions

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Palestinian 'geeks' code their route to a better future in Gaza | killexams.com actual questions and Pass4sure dumps

Tech incubator Gaza Sky Geeks describes itself as the first tech hub in the Gaza bare [Fedaa al-Qedra/Al Jazeera]

Gaza - When Yasmin Helles was an English literature student at a Gaza college, she would expend most of her time online looking for information that could aid her in academic life.

She always wondered who designed these websites, making every separate this information available.

She wanted to become that person.

Six months ago, the 24-year-old saw an advertisement by Gaza Sky Geeks (GSG), a rapidly growing industry and tech incubator, calling for adolescent graduates to enrol in the first coding school in the beleaguered Palestinian territory, which only recently saw yet another round of deadly Israeli air raids.

Helles took the unexpected step of quitting her job as an English teacher to expend more time pursuing her dream.

Now, she has joined the coding academy.

"I said to myself 'Yes, that was what I wanted,'" Helles told Al Jazeera in GSG's main room, a computer lab, taking a respite from typing lines of code.

I'm supercilious that I can now build a mobile app to serve a great slice of people who requisite it.

Ibrahim Al-Sheik, 23-year-old computer engineering graduate

Employment beyond the siege

Gaza is home to roughly 2 million people and experiences one of the highest unemployment rates in the world - more than 50 percent are without work.

The unemployment is a product of its isolation. Gaza has been under an Israeli blockade, assisted by Egypt under the governments of former President Hosni Mubarak and current leader Abdel Fattah el-Sisi, since 2007.

But Gazans are finding opportunities beyond the besieged strip. There is a ascend in entrepreneurial start-ups and tech accelerators, providing residents of the bare with outside opportunities previously unavailable.

GSG's coding school was established in 2017 with funding from the likes of Google and London-based coding boot camp Founders & Coders. It aims to empower students to live full-stack developers, which means they will live able to ply software edifice for mobile, computer and web.

Graduates learn to deploy production-grade software online and secure high-quality jobs with companies or labor as freelance developers.

To associate the academy, one doesn't fill to fill a prior technical background; it requires full-time availability, English language skills at an intermediate plane and the motivation to become a professional software developer. It teaches programming to anyone regardless of their specialisation and technical knowledge.

Gender parity, peer-led

The academy moreover values equality. Gender parity is required for those who enrol in its 16 seats, a rare thing in the conservative Gaza Strip.

Though Helles had no previous sustain in coding, she challenged herself and qualified for enrolment, winning a seat out of the eight of reserved for females.

In comparison, a Reuters report from May showed that about "one in three employees at Google, Facebook and Apple is a woman".

GSG's managers crow female participation in their programme is higher than in Silicon Valley, Helles said.

Helles expressed her pride in being one of the girls involved in the tech industry, which is largely a male-dominated, tiny realm in Gaza.

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The curriculum is taught in peer-led, project-based groups of four students. They earn up the teams which find problems and solve them in rational ways to finally design a website.

Students in the academy study the same curriculum taught in the Founder & Coders, a UK-based coding school.

Because the coding programme is fragment of an international organisation, alien mentors and facilitators can obtain Israeli permits to regularly enter Gaza to follow the process of the curriculum.

Once they complete the programme, graduates are competent full-stack developers able to labor in titanic teams, build complete prototypes in order to test their ideas, labor with clients and manage the life cycle of a product, Helles explained.

Gaza's already lofty unemployment is even worse among the youth, reaching 70 percent, according to recent numbers by the World Bank.

Ambitious and smart graduates relish Helles who are fortunate enough to attend GSG contemplate the coding boot camp as a sliver of hope that prevents them from joining the army of unemployed.

The academy follows graduates in the labour market, providing them with quarterly assessments and guidance on their work.

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GSG moreover tracks their progress in the market as a means of support, whether they are working in companies, ministries and banks or as freelancers who find labor online.

Providing opportunities

In addition to the blockade, and the recent flare-up of violence, the Gaza bare has suffered three Israeli wars that severely damaged its infrastructure. Israeli restrictions and intra-Palestinian fighting between Hamas and the West Bank-based Palestinian Authority impede infrastructure rehabilitation.

Electricity is available for four hours a day, water is undrinkable and travel is severely limited.

These issues worsen Gaza's dire economic situation. While generic unemployment sits at 50 percent, the youth unemployment rate is roughly 70 percent, economist and media official from the Gaza Chamber of Commerce and Industry, Maher Tabaa, told Al Jazeera.

Demand for jobs is higher than supply, Tabaa continued, causing graduates to labor at low wages despite their lofty qualifications.

There are about five tech and industry incubators and accelerators in Gaza, but there is no estimate of the job opportunities they create.

These tech accelerators fill helped Gaza find success in the realm of software and information technology despite the scarcity of possibilities, Tabaa continued.

Passing knowledge

Ibrahim Al-Sheik, 23-year-old computer engineering graduate from Gaza's Al-Azhar University, told Al Jazeera that writing code has proven to live the fastest route to find labor in the besieged strip.

His time with GSG not only helped him find work, but moreover equipped him with a skillset to succeed.

"It was a wonderful sustain that had a positive impact on my personality," Sheik said of his participation in the programming camp.

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Through his time at the academy, he learned skills relish "self-confidence, self-reliance, teamwork, and profitable communication with clients," every separate while improving his English. The Academy has added a lot to me," Sheik said.

Sheik is using these skills for the profit of others. He created a chat website to connect patients dealing with mental health issues with doctors who provide online consultations.

"I'm supercilious that I can now build a mobile app to serve a great slice of people who requisite it."

GSG's coding school is free, but enrolees are required to transfer what they learned to the students in the next cohort.

Sheik looks forward to passing the skills and scholarship he learned from the GSG programme.


In-Process Metrics for Software Testing | killexams.com actual questions and Pass4sure dumps

Although there are numerous metrics for software testing, and fresh ones being proposed frequently, relatively few are supported by sufficient experiences of industry implementation to demonstrate their usefulness. In this chapter from his book, Stephen Kan provides a minute discussion of some tried and actual in-process metrics from the testing perspective.

This chapter is from the bespeak 

In Chapter 9 they discussed attribute management models with examples of in-process metrics and reports. The models cover both the front-end design and coding activities and the back-end testing phases of development. The focus of the in-process data and reports, however, are geared toward the design review and code inspection data, although testing data is included. This chapter provides a more minute discussion of the in-process metrics from the testing perspective.1 These metrics fill been used in the IBM Rochester software evolution laboratory for some years with continual evolution and improvement, so there is ample implementation sustain with them. This is considerable because although there are numerous metrics for software testing, and fresh ones being proposed frequently, relatively few are supported by sufficient experiences of industry implementation to demonstrate their usefulness. For each metric, they dispute its purpose, data, interpretation, and use, and provide a lifelike sample based on real-life data. Then they dispute in-process attribute management vis-à-vis these metrics and revisit the metrics framework, the effort/outcome model, again with sufficient details on testing-related metrics. Then they dispute some practicable metrics for a special test scenario, acceptance test with admiration to vendor-developed code, based on the experiences from the IBM 2000 Sydney Olympics project by Bassin and associates (2002). Before they conclude the chapter, they dispute the pertinent question: How execute you know your product is profitable enough to ship?

Because the examples are based on IBM Rochester's experiences, it would live useful to contour IBM Rochester's software test process as the context, for those who are interested. The accompanying box provides a brief description.

In this section, they dispute the key in-process metrics that are effective for managing software testing and the in-process attribute status of the project.

10.1.1 Test Progress S Curve (Planned, Attempted, Actual)

Tracking the progress of testing is perhaps the most considerable tracking job for managing software testing. The metric they recommend is a test progress S curve over time. The X-axis of the S curve represents time units and the Y-axis represents the number of test cases or test points. By "S curve" they intend that the data are cumulative over time and resemble an "S" shape as a result of the term of vehement test activity, causing a steep planned test ramp-up. For the metric to live useful, it should hold the following information on one graph:

  • Planned progress over time in terms of number of test cases or number of test points to live completed successfully by week (or other time unit such as day or hour)

  • Number of test cases attempted by week (or other time unit)

  • Number of test cases completed successfully by week (or other time unit)

  • The purpose of this metric is to track test progress and compare it to the plan, and therefore live able to enmesh action upon early indications that testing activity is falling behind. It is well known that when the schedule is under pressure, testing, especially evolution testing, is affected most significantly. Schedule slippage occurs day by day and week by week. With a formal test progress metric in place, it is much more difficult for the team to ignore the problem. From the project planning perspective, an S curve forces better planning (see further discussion in the following paragraphs).

    IBM Rochester's Software Test Process

    IBM Rochester's systems software evolution process has a tenacious focus on the front-end phases such as requirements, architecture, design and design verification, code integration quality, and driver builds. For example, the completion of high-level design review (I0) is always a key event in the system schedule and managed as an intermediate deliverable. At the same time, testing (development tests and independent tests) and customer validation are the key process phases with equally tenacious focus. As device 10.1 shows, the common industry model of testing includes functional test, system test, and customer beta test before the product is shipped. Integration and solution testing can occur before or after the product ships. It is often conducted by customers because the customer's integrated solution may consist of products from different vendors. For IBM Rochester, the first test side after unit testing and code integration into the system library consists of component test (CT) and component regression test (CRT), which is equivalent to functional test. The next test side is system test (ST), which is conducted by an independent test group. To ensure entry criteria is met, an acceptance test (STAT) is conducted before system test start. The main path of the test process is from CT Æ CTR Æ STAT Æ ST. Parallel to the main path are several evolution and independent tests:

  • Along with component test, a stress test is conducted in a great network environment with performance workload running in the background to stress the system.

  • When significant progress is made in component test, a product-level test (PLT), which focuses on the subsystems of an overall integrated software system (e.g., database, client access, clustering), starts.

  • The network test is a specific product-level test focusing on communications subsystems and related oversight recovery processes.

  • The independent test group moreover conducts a software installation test, which runs from the middle of the component test until the conclude of the system test.

  • Figure 10.1Figure 10.1 IBM Rochester's Software Testing Phases

    The component test and the component regression test are done by the evolution teams. The stress test, the product-level test, and the network test are done by evolution teams in special test environments maintained by the independent test group. The install and system tests are conducted by the independent test team. Each of these different tests plays an considerable role in contributing to the lofty attribute of an integrated software system for the IBM eServer iSeries and AS/400 computer system. Later in this chapter, another shaded box provides an overview of the system test and its workload characteristics.

    As device 10.1 shows, several early customer programs occur at the back conclude of the evolution process:

  • Customer invitational program: Selected customer invited to the evolution laboratory to test the fresh functions and latest technologies. This is done when component and component regression tests are near completion.

  • Internal beta: The evolution site uses the latest release for its IT production operations (i.e., eating one's own cooking)

  • Beta program with industry partners

  • Customer beta program

  • Figure 10.2 is an sample of the component test metric at the conclude of the test of a major release of an integrated operating system. As can live seen from the figure, the testing intent is expressed in terms of a line curve, which is deliver in plot before the test begins. The bare bars argue the cumulative number of test cases attempted and the solid bars portray the number of successful test cases. With the intent curve in place, each week when the test is in progress, two bars (one for attempted and one for successful) are added to the graph. This sample shows that during the rapid test ramp-up term (the steep slope of the curve), for some weeks the test cases attempted were slightly ahead of intent (which is possible), and the successes were slightly behind plan.

    Figure 10.2Figure 10.2 Sample Test Progress S Curve

    Because some test cases are more considerable than others, it is not unusual in software testing to allocate scores to the test cases. Using test scores is a normalization approach that provides more accurate tracking of test progress. The assignment of scores or points is normally based on experience, and at IBM Rochester, teams usually utilize a 10-point scale (10 for the most considerable test cases and 1 for the least). To track test points, the teams requisite to express the test intent (amount of testing done every week) and track the week-by-week progress in terms of test points. The sample in device 10.3 shows test point tracking for a product plane test, which was underway, for a systems software. It is famed that there is always an component of subjectivity in the assignment of weights. The weights and the resulting test points should live determined in the test planning stage and remain unchanged during the testing process. Otherwise, the purpose of this metric will live compromised in the reality of schedule pressures. In software engineering, weighting and test score assignment remains an spicy locality where more research is needed. practicable guidelines from such research will surely profit the planning and management of software testing.

    Figure 10.3Figure 10.3 Test Progress S Curve—Test Points Tracking

    For tracking purposes, test progress can moreover live weighted by some measurement of coverage. Coverage weighting and test score assignment consistency become increasingly considerable in balance to the number of evolution groups involved in a project. requisite of attention to tracking consistency across functional areas can result in a delusory view of the overall project's progress.

    When a intent curve is in place, the team can set up an in-process target to reduce the risk of schedule slippage. For instance, a disparity target of 15% between attempted (or successful) and planned can live used to trigger additional actions. Although the test progress S curves, as shown in Figures 10.2 and 10.3, give a quick visual status of the progress against the total intent and plan-to-date (the eye can quickly determine if testing is ahead or behind on planned attempts and successes), it may live difficult to discern the exact amount of slippage. This is particularly actual for great testing efforts, where the number of test cases is in the hundreds of thousands. For that reason, it is useful to moreover panoply the test status in tabular form, as in Table 10.1. The table moreover shows underlying data broken out by department and product or component, which helps to identify problem areas. In some cases, the overall test curve may materialize to live on schedule, but when progress is viewed only at the system level, because some areas are ahead of schedule, they may mask areas that are behind schedule. Of course, test progress S curves are moreover used for functional areas and for specific products.

    table 10.1 Test Progress Tracking—Planned, Attempted, Successful

    No. of Test Cases Planned to Date

    Percent of intent Attempted

    Percent of intent Successful

    No. of Planned Test Cases Not Yet Attempted

    Percent of Total Attempted

    Percent of Total Successful

    System

    60577

    90.19

    87.72

    5940

    68.27

    66.10

    Dept A

    1043

    66.83

    28.19

    346

    38.83

    15.60

    Dept B

    708

    87.29

    84.46

    90

    33.68

    32.59

    Dept C

    33521

    87.72

    85.59

    4118

    70.60

    68.88

    Dept D

    11275

    96.25

    95.25

    423

    80.32

    78.53

    Dept E

    1780

    98.03

    94.49

    35

    52.48

    50.04

    Dept F

    4902

    100.00

    99.41

    0

    96.95

    95.93

    Product A

    13000

    70.45

    65.10

    3841

    53.88

    49.70

    Product B

    3976

    89.51

    89.19

    417

    66.82

    66.50

    Product C

    1175

    66.98

    65.62

    388

    32.12

    31.40

    Product D

    277

    0

    0

    277

    0

    0

    Product E

    232

    6.47

    6.470

    214

    3.78

    3.70

    An initial intent curve should live topic to brainstorming and challenges. For example, if the curve shows a very steep ramp-up in a short term of time, the project manager may challenge the team with respect to how doable the intent is or the team's specific planned actions to execute the intent successfully. As a result, better planning will live achieved. Caution: Before the team settles on a intent curve and uses it to track progress, a captious evaluation of what the intent curve represents must live made. Is the total test suite considered effective? Does the intent curve portray lofty test coverage (functional coverage)? What are the rationales for the sequences of test cases in the plan? This nature of evaluation is considerable because once the intent curve is in place, the visibility of this metric tends to draw the all team's attention to the disparity between attempted, successful, and the planned testing.

    Once the intent line is set, any proposed or actual changes to the intent should live reviewed. intent slips should live evaluated against the project schedule. In general, the baseline intent curve should live maintained as a reference. Ongoing changes to the planned testing schedule can mask schedule slips by indicating that attempts are on track, while the intent curve is actually stirring to the right.

    In addition, this metric can live used for release-to-release or project-to-project comparisons, as the sample in device 10.4 shows. For release-to-release comparisons, it is considerable to utilize time units (weeks or days) before product ship (or generic availability, GA) as the unit for the X-axis. By referencing the ship dates, the comparison provides a actual status of the project in process. In device 10.4, it can live observed that Release B, represented by the dotted line, is more back-end loaded than Release A, which is represented by the solid line. In this context, the metric is both a attribute and a schedule statement for the testing of the project. This is because late testing causes late cycle defect arrivals and therefore negatively affects the attribute of the final product. With this nature of comparison, the project team can intent ahead (even before the testing starts) to mitigate the risks.

    Figure 10.4Figure 10.4 Test intent Curve—Release-to-Release Comparison

    To implement this metric, the test execution intent needs to live laid out in terms of the weekly target, and actual data needs to live tracked on a weekly basis. For wee to medium projects, such planning and tracking activities can utilize common tools such as Lotus 1-2-3 or other project management tools. For great and intricate projects, a stronger tools advocate facility normally associated with the evolution environment may live needed. Many software tools are available for project management and attribute control, including tools for defect tracking and defect projections. Testing tools usually include test library tools for keeping track of test cases and for test automation, test coverage analysis tools, test progress tracking, and defect tracking tools.

    10.1.2 Testing Defect Arrivals over Time

    Defect tracking and management during the testing side is highly recommended as a gauge exercise for every separate software testing. Tracking testing progress and defects are common features of many testing tools. At IBM Rochester, defect tracking is done via the problem tracking report (PTR) tool. They fill discussed PTR-related models and reports previously. In this chapter they revisit two testing defect metrics (arrivals and backlog) with more details. They recommend tracking the defect arrival pattern over time, in addition to tracking by test phase. Overall defect density during testing, or for a particular test, is a summary indicator, but not really an in-process indicator. The pattern of defect arrivals over time gives more information. As discussed in Chapter 4 (section 4.2.2), even with the same overall defect rate during testing, different patterns of defect arrivals may imply different scenarios of realm quality. They recommend the following for this metric:

  • Always include data for a comparable baseline (a prior release, a similar project, or a model curve) in the chart if such data is available. If a baseline is not available, at the minimum, when tracking starts, set some expected plane of defect arrivals at key points of the project schedule (e.g., midpoint of functional test, system test entry, etc.).

  • The unit for the X-axis is weeks (or other time units ) before product ship

  • The unit for the Y-axis is the number of defect arrivals for the week, or its variants.

  • Figure 10.5 is an sample of this metric for releases of an integrated operating system. For this example, the main goal is release-to-release comparison at the system level. The metric can live used for the defect arrival patterns based on the total number of defects from every separate test phases, and for defect arrivals for specific tests. It can live used to compare actual data with a PTR arrival model, as discussed in Chapter 9.

    Figure 10.5Figure 10.5 Testing Defect Arrival Metric

    Figure 10.5 has been simplified for presentation. The actual graph has much more information on it including plumb lines to depict the key dates of the evolution cycle and system schedules such as terminal fresh duty integration, evolution test completion, start of system test, and so forth. There are moreover variations of the metric: total defect arrivals, strict defects (e.g., severity 1 and 2 defects in a 4-point severity scale), defects normalized to size of the release (new and changed code plus a partial weight for ported code), and total defect arrivals versus convincing defects. The main, and the most useful, chart is the total number of defect arrivals. In their projects, they moreover include a lofty severity (severity 1 and 2) defect chart and a normalized view as mainstays of tracking. The normalized defect arrival chart can liquidate some of the visual guesswork of comparing current progress to historical data. In conjunction with the severity chart, a chart that displays the percentage of severity 1 and 2 PTRs per week can live useful. As device 10.6 shows, the percentage of lofty severity problems increases as the release progresses toward the product ship date. Generally, this is because the urgency for problem resolution increases when approaching product delivery, therefore, the severity of the defects was elevated. Unusual swings in the percentage of lofty severity problems, however, could signal earnest problems and should live investigated.

    Figure 10.6Figure 10.6 Testing Defect Arrivals—Percentage of Severity 1 and 2 Defects

    When execute the defect arrivals peak relative to time to product delivery? How does this pattern compare to previous releases? How lofty execute they peak? execute they decline to a low and stable plane before delivery? Questions such as these are key to the defect arrival metric, which has significant attribute implications for the product in the field. A positive pattern of defect arrivals is one with higher arrivals earlier, an earlier peak (relative to the baseline), and a decline to a lower plane earlier before the product ship date, or one that is consistently lower than the baseline when it is confident that the effectiveness of testing is at least as profitable as previous testing. The tail conclude of the curve is especially considerable because it is indicative of the attribute of the product in the field. lofty defect activity before product delivery is more often than not a mark of attribute problems. To interpret the defect arrivals metrics properly, refer to the scenarios and questions discussed in Chapter 4 section 4.2.1.

    In addition to being an considerable in-process metric, the defect arrival pattern is the data source for projection of defects in the field. If they change from the weekly defect arrival curve (a density configuration of the metric) to a cumulative defect curve (a cumulative distribution configuration of the metric), the curve becomes a well-known configuration of the software reliability growth pattern. Specific reliability models, such as those discussed in Chapters 8 and 9, can live applied to the data to project the number of residual defects in the product. device 10.7 shows such an example. The actual testing defect data represents the total cumulative defects removed when every separate testing is complete. The fitted model curve is a Weibull distribution with the shape parameter (m) being 1.8. The projected latent defects in the realm is the disagreement in the Y-axis of the model curve between the product ship date and when the curve is approaching its limit. If there is a time disagreement between the conclude date of testing and the product ship date, such as this case, the number of latent defects represented by the section of the model curve for this time segment has to live included in the projected number of defects in the field.

    Figure 10.7Figure 10.7 Testing Defect Arrival Curve, Software Reliability Growth Model, and Defect Projection

    10.1.3 Testing Defect Backlog over Time

    We define the number of testing defects (or problem tracking reports, PTRs) remaining at any given time as the defect backlog (PTR backlog). Simply put, defect backlog is the accumulated disagreement between defect arrivals and defects that were closed. Defect backlog tracking and management is considerable from the perspective of both test progress and customer rediscoveries. A great number of outstanding defects during the evolution cycle will impede test progress. When a product is about to ship to customers, a lofty defect backlog means more customer rediscoveries of the defects already organize during the evolution cycle. For soft-ware organizations that fill sunder teams to conduct evolution testing and to fix defects, defects in the backlog should live kept at the lowest practicable plane at every separate times. For organizations that fill the same teams amenable for evolution testing and fixing defects, however, there are commandeer timing windows in the evolution cycle for which the priority of focuses may vary. While the defect backlog should live managed at a reasonable plane at every separate times, it should not live the highest priority during a term when making headway in functional testing is the critical-path evolution activity. During the prime time for evolution testing, the focus should live on test effectiveness and test execution, and defect discovery should live encouraged to the maximum practicable extent. Focusing too early on overall defect backlog reduction may contest with these objectives. For example, the evolution team may live inclined not to open defect records. The focus during this time should live on the fix turnaround of the captious defects that impede test progress instead of the entire backlog. Of course, when testing is approaching completion, tenacious focus for drastic reduction in the defect backlog should enmesh place.

    For software evolution projects that build on existing systems, a great backlog of "aged" problems can develop over time. These aged defects often portray fixes or enhancements that developers believe would legitimately ameliorate the product, but which glean passed over during evolution due to resource or design constraints. They may moreover portray problems that fill been fixed or are obsolete as a result of other changes. Without a concerted effort, this aged backlog can build over time. This is one locality of the defect backlog that warrants attention early in the evolution cycle, even prior to the start of evolution testing.

    Figure 10.8 is an sample of the defect backlog metric for several releases of a systems software product. Again, release-to-release comparisons and actual data versus targets are the main objectives. Target X was a point target for a specific event in the project schedule. Target Y was for the term when the product was being readied to ship.

    Figure 10.8Figure 10.8 Testing Defect Backlog Tracking

    Note that for this metric, a sole focus on the numbers is not sufficient. In addition to the overall reduction, deciding which specific defects should live fixed first is very considerable in terms of achieving early system stability. In this regard, the expertise and ownership of the evolution and test teams are crucial.

    Unlike defect arrivals, which should not live controlled artificially, the defect backlog is completely under the control of the evolution organization. For the three metrics they fill discussed so far, they recommend the following overall project management approach:

  • When a test intent is in plot and its effectiveness evaluated and accepted, manage test progress to achieve an early ramp-up in the S curve.

  • Monitor defect arrivals and analyze the problems (e.g., defect cause analysis and Pareto analysis of problem areas of the product) to gain scholarship for improvement actions. execute not artificially control defect arrivals, which is a duty of test effectiveness, test progress, and the intrinsic attribute of the code (the amount of latent defects in the code). execute inspirit opening defect records when defects are found.

  • Strongly manage defect backlog reduction and achieve predetermined targets associated with the fix integration dates in the project schedule. Known defects that impede testing progress should live accorded the highest priority.

  • The three metrics discussed so far are obviously related, and they should live viewed together. We'll near back to this point in the section on the effort/outcome model.

    10.1.4 Product Size over Time

    Lines of code or another indicator of the project size that is meaningful to the evolution team can moreover live tracked as a gauge of the "effort" side of the evolution equation. During product development, there is a inclination toward growth as requirements and designs are fleshed out. Functions may continue to live added to meet late requirements or the evolution team wants more enhancements. A project size indicator, tracked over time, can serve as an explanatory factor for test progress, defect arrivals, and defect backlog. It can moreover relate the measurement of total defect volume to per unit improvement or deterioration. device 10.9 shows a project's release size pattern with rapid growth during release definition, stabilization, and then possibly a slight reduction in size toward release completion, as functions that fail to meet schedule or attribute objectives are deferred. In the figure, the different segments in the bars portray the different layers in the software system. This metric is moreover known as an indicator of scope creep. Note that lines of code is only one of the size indicators. The number of duty points is another common indicator, especially in application software. They fill moreover seen the number of bytes of recollection that the software will utilize as the size indicator for projects with embedded software.

    Figure 10.9Figure 10.9 Lines of Code Tracking over Time

    10.1.5 CPU Utilization During Test

    For computer systems or software products for which a lofty plane of stability is required to meet customers' needs, it is considerable that the product discharge well under stress. In software testing during the evolution process, the plane of CPU utilization is an indicator of the system's stress.

    To ensure that its software testing is effective, the IBM Rochester software evolution laboratory sets CPU utilization targets for the software stress test and the system test. Stress testing starts at the middle of the component test side and may elude into the system test time frame with the purpose of stressing the system in order to uncover latent defects that cause system crashes and hangs that are not easily discovered in prevalent testing environments. It is conducted with a network of systems. System test is the final test side with a customerlike environment. Test environment, workload characteristics, and CPU stress plane are major factors contributing to the effectiveness of the test. The accompanying box provides an overview of the IBM Rochester system test and its workload characteristics.

    System Test Overview and Workload Characteristics

    IBM Rochester's system test serves as a means to provide a predelivery readiness assessment of the product's competence to live installed and operated in customerlike environments. These test environments focus on the total solution, including current release of the operating system, fresh and existing hardware, and customerlike applications. The resulting test scenarios are written to exercise the operating system and related products in a manner similar to customers' businesses. These simulated environments execute not attempt to replicate a particular customer, but portray a composite of customer types in the target market.

    The model used for simulating customerlike environments is referred to as the RAISE (Reliability, Availability, Installability, Serviceability, and Ease of use) environment. It is designed to portray an interrelated set of companies that utilize the IBM products to advocate and drive their day-to-day industry activities. Test scenarios are defined to simulate the different types of end-user activities, work-flow, and industry applications. They include CPU-intensive applications and interaction-intensive computing. During test execution, the environment is elude as a 24-hour-a-day, 7-day-a-week (24x7) operation.

    Initially, labor items are defined to address complete solutions in the RAISE environment. From these labor items near more minute scenario definitions. These scenarios are written to elude in the respective test environment, performing a sequence of tasks and executing a set of test applications to depict some customerlike event. Scenario variations are used to cater test distress to different workloads, operating environments, and run-time duration. The resulting interaction of multiple scenarios executing across a network of systems provides a representation of actual end-user environments. This provides an assessment of the overall functionality in the release, especially in terms of customer solutions.

    Some areas that scenario testing concentrates on include:

  • Compatibility of multiple products running together

  • Integration and interoperability of products across a intricate network

  • Coexistence of multiple products on one hardware platform

  • Areas of potential customer dissatisfaction:

  • Unacceptable performance

  • Unsatisfactory installation

  • Migration/upgrade difficulties

  • Incorrect and/or difficult-to-use documentation

  • Overall system usability

  • As is the case for many customers, most system test activities require more than one system to execute. This fact is essential to understand, from both product integration and usage standpoints, and moreover because this represents a more realistic, customerlike setup. In driving multiple, interrelated, and concurrent activities across their network, they watch to "shake out" those hard-to-get-at latent problems. In such a intricate environment, these types of problems watch to live difficult to analyze, debug, and fix, because of the layers of activities and products used. Additional distress to fix these problems is time well spent, because many of them could easily become captious situations to customers.

    Workloads for the RAISE test environments are defined to plot an stress on stressful, concurrent product interaction. Workload characteristics include:

  • Stressing some of the more intricate fresh features of the system

  • Running automated tests to provide background workload for additional concurrence and stress testing and to test previous release duty for regression

  • Verifying that the software installation instructions are accurate and understandable and that the installation duty works properly

  • Testing release-to-release compatibility, including n to n-1 communications connectivity and system interoperability

  • Detecting data conversion problems by simulating customers performing installations from a prior release

  • Testing availability and recovery functions

  • Artistic testing involving cataclysm and oversight recovery

  • Performing policy-driven system maintenance (e.g., backup, recovery, and applying fixes)

  • Defining and managing different security levels for systems, applications, documents, files, and user/group profiles

  • Using the tools and publications that are available to the customer or IBM service personnel when diagnosing and resolving problems

  • Another objective during the RAISE system test is to maintain customer environment systems at stable hardware and software levels for an extended time (one month or more). A guideline for this would live minimum number of unplanned initial program loads (IPL, or reboot) except for maintenance requiring an IPL. The intent is to simulate an lively industry and detect problems that occur only after the systems and network fill been operat

    The data in device 10.10 argue the recent CPU utilization targets for the IBM Rochester's system test. Of the five systems in the system test environment, there is one system with a 2-way processor (VA), two systems with 4-way processors (TX and WY), and one system each with 8-way and 12-way processors. The upper CPU utilization limits for TX and WY are much lower because these two systems are used for interactive processing. For the overall testing network, the baseline targets for system test and the acceptance test of system test are moreover shown.

    Figure 10.10Figure 10.10 CPU Utilization Targets for Testing Systems

    The next example, shown in device 10.11, demonstrates the tracking of CPU utilization over time for the software stress test. There is a two-phase target as represented by the step-line in the chart. The original target was set at 16 CPU hours per system per day on the average, with the following rationale:

  • The stress test runs 20 hours per day, with 4 hours of system maintenance.

  • The CPU utilization target is 80% or higher.

  • Figure 10.11Figure 10.11 CPU Utilization Metrics

    The second side of the target, set at 18 CPU hours per system per day, is for the back conclude of the stress test. As the device shows, a key component of this metric, in addition to comparison of actual and target data, is release-to-release comparison. One can observe that the curve for release C had more data points in the early evolution cycle, which were at higher CPU utilization levels. This is because pretest runs were conducted prior to availability of the fresh release content. For every separate three releases, the CPU utilization metric shows an increasing trend with the stress test progress. The CPU utilization metric is used together with the system crashes and hangs metric. This relationship is discussed in the next section.

    To collect CPU utilization data, a performance monitor appliance runs continuously (24x7) on each test system. Through the communication network, the data from the test systems are sent to a nontest system on a real-time basis. By means of a Lotus Notes database application, the final data can live easily tallied, displayed, and monitored.

    10.1.6 System Crashes and Hangs

    Hand in hand with the CPU utilization metric is the system crashes and hangs metric. This metric is operationalized as the number of unplanned initial program loads (IPLs, or reboots) because for each crash or hang, the system has to live re-IPLed (rebooted). For software tests whose purpose is to ameliorate the stability of the system, they requisite to ensure that the system is stressed and testing is conducted effectively to uncover latent defects that would lead to system crashes and hangs, or in generic any unplanned IPLs. When such defects are discovered and fixed, stability of the system improves over time. Therefore, the metrics of CPU utilization (stress level) and unplanned IPLs record the distress aspect and the outcome aspect respectively, of the effectiveness of the test.

    Figure 10.12 shows the system crashes and hangs metric for the same three releases shown in device 10.11. The target curve was derived based on data from prior releases by fitting an exponential model.

    Figure 10.12Figure 10.12 System Crashes and Hangs Metric

    In terms of data collection, when a system crash or hang occurs and the tester reboots (re-IPLs) the system, the performance monitor and IPL tracking appliance produces a screen prompt and requests information about the terminal system crash or hang. The tester can ignore the prompt temporarily, but it will reappear regularly after a confident time until the questions are answered. Information elicited via this appliance includes test system, network ID, tester name, IPL code and intuition (and additional comments), system reference code (SRC) if available, data and time system went down, release, driver, PTR number (the defect that caused the system crash or hang), and the denomination of the product. The IPL intuition code consists of the following categories:

  • 001 Hardware problem (unplanned)
  • 002 Software problem (unplanned)
  • 003 Other problem (unplanned)
  • 004 Load fix (planned)
  • Because the volume and trend of system crashes and hangs are germane to the stability of the product in the field, they highly recommend this in-process metric for software for which stability is an considerable attribute. These data should moreover live used to earn release-to-release comparisons and as leading indicators to product delivery readiness. While CPU utilization tracking definitely requires a tool, tracking of system crashes and hangs can start with pencil and paper if a disciplined process is in place.

    10.1.7 intend Time to Unplanned IPL

    Mean time to failure (MTTF), or intend time between failures (MTBF), are the gauge measurements of reliability. In software reliability literature, this metric and various models associated with it fill been discussed extensively. Predominantly, the discussions and utilize of this metric are related to academic research or specific-purpose software systems. To the author's awareness, implementation of this metric is rare in organizations that develop commercial systems. This may live due to several reasons including issues related to single-system versus multiple-systems testing, the definition of a failure, the feasibility and cost in tracking every separate failures and minute time-related data (Note: Failures are different from defects or faults; a separate defect can cause multiple failures and in different machines) in commercial projects, and the value and revert on investment of such tracking.

    System crashes and hangs (unplanned IPLs) are the more strict forms of failure. Such failures are clear-cut and easier to track, and metrics based on such data are more meaningful. Therefore, at IBM Rochester, they utilize intend time to unplanned IPL (MTI) as the software reliability metric. This metric is used only during the system testing period, which, as previously described, is a customerlike system integration test prior to product delivery. Using this metric for other tests earlier in the evolution cycle is practicable but will not live as meaningful because every separate the components of the system cannot live addressed collectively until the final system test. The formula to calculate the MTI metric is:

    where

    n = -Number of weeks that testing has been performed (i.e., the current week of test)

    H = Total of weekly CPU elude hours W = Weighting factor I = Number of weekly (unique) unplanned IPLs (due to software failures)

    Basically the formula takes the total number of CPU elude hours for each week (Hi), divides it by the number of unplanned IPLs plus 1 (Ii + 1), then applies a set of weighting factors to glean the weighted MTI number, if weighting is desired. For example, if the total CPU elude hours from every separate test systems for a specific week was 320 CPU hours and there was one unplanned IPL due to a system crash, then the unweighted MTI for that week would live 320/(1+1) = 160 CPU hours. In the IBM Rochester implementation, they apply a set of weighting factors based on results from prior baseline releases. The purpose of weighting factors is to enmesh the outcome from the prior weeks into account so that at the conclude of the system test (with a duration of 10 weeks), the MTI represents an entire system test statement. It is the practitioner's decision whether to utilize a weighting factor or how to divide the weights heuristically. Deciding factors may include nature of products and systems under test, test cycle duration, and how the test term is planned and managed.

    Figure 10.13 is an sample of the MTI metric for the system test of a recent release of an integrated operating system. The X-axis represents the number of weeks before product ship. The Y-axis on the prerogative side is MTI and on the left side is the number of unplanned IPLs. Inside the chart, the shaded areas portray the number of unique unplanned IPLs (crashes and hangs) encountered. From the start of the acceptance test of the system test, the MTI metric is shown tracking to intent until week 10 before product ship, when three system crashes occurred during one week. From the significant drop of the MTI, it was evident that with the original test plan, there would not live enough burn-in time for the system to compass the MTI target. Because this requisite of burn-in time might result in undetected captious problems, additional testing was done and the system test was lengthened by three weeks. The product ship date remained unchanged.

    Figure 10.13Figure 10.13 intend Time to Unplanned IPL Metric

    Clearly, discrepancies between actual and targeted MTI should trigger early, proactive decisions to adjust testing plans and schedules to earn confident that product ship criteria for burn-in can live achieved. At a minimum, the risks should live well understood and a risk mitigation intent should live developed. Action plans might include:

  • Extending test duration and/or adding resources

  • Providing for a more exhaustive regression test term if one were planned

  • Adding a regression test if one were not planned

  • Taking additional actions to intensify problem resolution and fix turnaround time (assuming that there is enough time available until the test cycle is planned to end)

  • 10.1.8 captious Problems: Showstoppers

    This showstopper parameter is very considerable because the severity and impact of software defects varies. Regardless of the volume of total defect arrivals, it takes only a few showstoppers to render a product dysfunctional. This metric is more qualitative than the metrics discussed earlier. There are two aspects of this metric. The first is the number of captious problems over time, with release-to-release comparison. This dimension is quantitative. The second, more important, dimension is concerned with the types of the captious problems and the analysis and resolution of each problem.

    The IBM Rochester's implementation of this tracking and focus is based on the generic criteria that any problem that will impede the overall progress of the project or that will fill significant impact on customer's industry (if not fixed) belongs to such a list. The tracking normally starts at the middle of the component test side when a captious problem meeting by the project management team (with representatives from every separate functional areas) takes plot once a week. When it gets closer to system test and product delivery time, the focus intensifies and daily meetings enmesh place. The objective is to facilitate cross-functional teamwork to resolve the problems swiftly. Although there is no formal set of criteria, problems on the captious problem list watch to live problems related to installation, system stability, security, data corruption, and so forth. every separate problems on the list must live resolved before product delivery.


    The Core Activities of Performance Testing | killexams.com actual questions and Pass4sure dumps

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    There are seven core activities that occur in a successful performance testing process. Before implementing them, it is considerable that they understand these seven concepts.

    The core activities are as follows:

    Identification of Test Environments

    Here they identify the physical test and production environment for the software application. It moreover identifies the tools and resources that are available to the test team. The environment, tools and resources here refer to the configurations and settings of the hardware, the software and the network. A thorough understanding of this test environment enables better test planning and design. This identification process needs to live periodically reviewed during the testing process.

    The key factors to reckon for test environment identification are as follows:

    Identification of Performance Acceptance Criteria

    This step involves identifying or estimating the performance characteristics of the software application. This starts with noting the performance characteristics which are rendered as profitable performance by the stakeholders. The main characteristics of a stable performance are Response Time, Resource Utilization and Throughput.

    The key factors to reckon for identification of performance acceptance criteria are as follows:

  • Business Requirements and obligations
  • User Expectations
  • Industry Standards and Regulatory Compliance Criteria
  • Service plane Agreements (SLAs)
  • Resource Utilization Limits
  • Workload Models
  • Anticipated load Conditions
  • Stress Conditions
  • Performance Indicators
  • Previous Releases
  • Competing Applications
  • Objectives of Optimization
  • Safety and scalability
  • Schedule, Budget, Resources and Staffing
  • Plan and Design tests

    When you design and intent a test for quantifying the performance characteristics, actual world simulations should live created. This will generate significantly apropos and useful results that will aid the organization to enmesh informed industry decisions. If this is not the test objective, then the most valuable usage scenarios should live explicitly determined.

    The key factors to reckon in planning and designing tests are as follows:

  • Obligated usage scenario

  • Usage scenarios implied by the testing goals

  • Most common usage scenarios.

  • Performance captious Usage scenarios

  • Technical Usage scenario

  • Stakeholder Usage scenario

  • High Visibility Usage Scenario

  • Business captious Usage ScenarioConfiguration of Test Environment

  • Innumerable issues arise from network, hardware, server operating systems and software compatibility. Configuring the test environment needs to live started early. This ensures that the configuration issues are resolved before the testing is begun. Additionally, occasional reconfiguration, updates, enhancements should live carried out throughout the project lifecycle.

  • The key factors to reckon in configuring the test environment are as follows:

  • Determine the maximum load that can live generated before reaching a load bottleneck.

  • Verify the synchronization of every separate the system clocks from where the data resources are collected.

  • Validate the load testing accuracy against the different hardware components.

  • Validate the load testing accuracy against the server clusters.

  • Validate the distribution of load by monitoring the resource utilization across servers.

  • Implementation of Test Design

    The biggest challenge in performance testing is to execute a realistic test with simulated data in a route that the application being tested cannot differentiate between actual data and simulated data.

  • The key factors to reckon for implementation of test design are as follows:

  • Ensure the reform implementation of the test data feeds.

  • Ensure the reform implementation of transaction validations.

  • Ensure the reform handling of hidden information fields and special data

  • Validate the key performance indicators.

  • Ensure the proper population of variables for request parameters.

  • Consider request wrapping in test scripts to measure the response time for requests.

  • Consider the script to match the designed test compared to changing the test to match the script.

  • Evaluate the generated results against those expected. This validates the script development.

  • Execute Tests

    The process of executing test cases is conditional on the tools, resources and the environment. It can live said to live a combination of the following tasks:

  • Coordinating the execution of tests.

  • Validating the tests, configurations and the data environments.

  • Executing tests.

  • Validating and monitoring the scripts and data while executing.

  • Reviewing the results on test completion

  • Archiving the tests, test data, test results and related information for later use.

  • Logging activity times for later identification.

  • The key factors to reckon while executing tests are as follows:

  • Validate the execution of tests for completed data.
  • Validate the utilize of reform values of data for realistic simulation of the industry scenario.
  • Limit the test execution cycles and review them after each cycle.
  • Execute the same test multiple times to determine the factors accounting for the difference.
  • Observe any unusual deportment while test execution.
  • Set up warning to the team before executing tests.
  • Do not carry out extra processes on the load generating machine while generating a load.
  • Simulate ramp up and icy down periods.
  • Executing a test can live held up when a point of diminishing returns is reached.
  • Analyze, Report, and Retest

    The main point of executing tests is more than the results. Conclusions requisite to live derived from them along with the consolidated data to advocate the conclusions. This process requires analysis, comparisons and reporting.

  • The key factors to reckon are follows:

  • Analyze the data both individually and collectively.

  • Analyze and compare the results to determine the inward or outward trend of the application under test.

  • If any fixes are made, then validate the fix by repeating the test.

  • Share the results of the test and earn the raw data available to the team.

  • Modify tests if desired objective is not met.

  • Exercise caution while reducing test data as valuable data cannot live lost.

  • Report early and often.

  • Report visually and intuitively.

  • Consolidate data correctly and summarize them effectively.

  • Intermediate reports should include priorities, issues and limitations for the next execution cycles.

  • Conclusion

    The above testing activities occur at different stages of the testing process. It is very considerable to understand the weight and objective of each activity elaborately to live able to design them to best fitting the project context.

    Topics:

    software testing ,performance testing ,quality assurance ,load testing



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    MORGAN Studio

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