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ISEBSWTINT-001 exam Dumps Source : ISEB Software Testing Intermediate

Test Code : ISEBSWTINT-001
Test title : ISEB Software Testing Intermediate
Vendor title : ISEB
: 25 real Questions

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ISEB ISEB Software Testing Intermediate

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The ISEB verify administration certification is the next level of competency in software testing after the ISEB foundation and ISEB Intermediate ranges. incomes this certification makes employment candidates extra constructive and marketable, as it signifies a robust capability to analyse, synthesize and evaluate initiatives related to software trying out administration. These potential are considerable to the career development of software testers, especially during this age of international competition and monetary uncertainty.

like every UniversalExams.com working towards packages, an accepted and particularly experienced ISEB route tutor authored the ‘test management’ course. The path is absolutely in accordance with the trustworthy ISEB examination syllabus and includes pleasing getting to know tools confirmed to maximise the absorption of the fabric.  This and entire other frequent exams practicing kits comprehend an ironclad, a hundred per cent guarantee: flow, or derive a full refund, no questions requested.

“UniversalExams.com turned into fashioned in 2001 with the end of creating QA certification reasonably priced,” notable advertising and marketing manager David L Brister. “previously, the only genuine strategy to prepare for a application tester certification exam became to attend a pricey two or three day instructor-led course. Their applications are inevitable to deliver the identical level of coaching, at a fraction of the expense.”

The test administration spy at package comprises a finished spy at ebook, follow exams, 56 twin-sided flashcards, scenario query papers, exam suggestions, premium material, ninety days full practicing back in the inner most enviornment of the business’s IT certification forum, and more. additionally, the kit is attainable in numerous delivery methods – immediate download, set of CDs, or every thing offered in a bound, full umbra booklet (with the premium cloth on a CD).

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

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

ESPOO, Finland, Jan. 14, 2002 (PRIMEZONE) -- TietoEnator is one of two Swedish corporations authorised to certify testers in line with the ISEB groundwork certificates for application checking out. The ISEB verify practicing could subsist provided in Sweden and Norway from January.

TietoEnator has its own test academics and presents the route to purchasers and personnel. it's a 3-day course, and at the finish of day three the participants can determine to buy an exam and derive the ISEB-certification.

- they now occupy observed an expanding demand for licensed testers, and when you account that there is no Swedish common for verify, they occupy chosen to deliver the ISEB groundwork certificates, says Thomas Klarbrant, Managing Director of TietoEnator verify solutions.

ISEB (assistance programs Examination Board) is a division within BCS (British computer Society). ISEB presents certifications within a pair of several IT areas. The point of ISEB is to raise the necessities within the IT company and to succor competence construction.

For further tips, gratify contact: Kennet Osbjer, TietoEnator check options, Sweden, +46 706 24 65 33 Marit Saelemyr, TietoEnator Consulting AS, Norway, +47 553 64468

With over 10,000 personnel and annual web sales of EUR 1.1 billion, TietoEnator is a number one organisation of extravagant cost-added IT services in Europe. TietoEnator specializes in consulting, structure and hosting its consumers' company operations in the digital monetary system. The group's capabilities are based on a combination of profound industry-particular talents and newest assistance know-how. www.tietoenator.com

TIETOENATOR 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 examine options, Sweden: Kennet Osbjer +forty six 706 24 65 33 TietoEnator Consulting AS, Norwa: Marit Saelemyr +47 553 64468

Maveric techniques Achieves assistance programs Examinations Board (ISEB) Accreditation | killexams.com real Questions and Pass4sure dumps

Maveric programs, one in entire India's accurate three impartial application trying out groups with a robust focus within the banking, fiscal features and coverage (BFSI) vertical, today introduced that it has been approved with the aid of the uk – based, counsel systems Examinations Board (ISEB) of The British laptop Society (BCS). recognized via corporate properties in Europe as a pre-requisite for software checking out specialists, the coveted ISEB accreditation affirms that Maveric has matured content material, methods and birth mechanisms for testing competency construction.

Maveric at this aspect of time has a huge presence in the UK and is within the manner of providing pilots in the onsite-offshore model. The capabilities companions comprehend well-liked system integrators within the zone of assurance, telecom and healthcare.

spoke of Mr. Ranga Reddy, CEO, Maveric techniques, "UK is an incredibly captious market in Maveric's growth approach with their operations doubling every quarter. As competition becomes greater extravagant during this geography, this acclaimed accreditation gives us a several capabilities, as they are the handiest Indian business working in UK with this accreditation".

Mr. Ranga Reddy introduced, "Maveric has fully met its monetary projections for 2005-06 and centered a sound base in the hub East and US. Projections for the subsequent 12 months are to the tune of seven Million greenbacks with a sturdy accent on offshore delivery. additional in 2006-07, they will subsist investing considerably in productisation of Maveric's proprietary examine frameworks, within the banking vertical".

About Maveric programs

Maveric techniques Ltd. is one in every of India's properly three independent application-testing companies with a robust and dedicated focal point in the banking, economic services and coverage verticals for the judgement that the year 2000. during the ultimate 5 years, Maveric has been pioneering the outsourcing model in application trying out by using virtue of its insightful potential within the banking and monetary features vertical, mighty core administration group and multi-locational testing facilities. As a leading pure-play, unbiased application checking out enterprise, Maveric drastically enhances the functionality, usability and performance of IT options, empowering its customers with a inevitable impregnate competencies. Maveric's superb customer profile includes a wide selection of leading software product groups, outfit integrators and fiscal institutions. With employee power of 300, Maveric has a significant presence in Bangalore, Chennai, Dubai, Hyderabad, London and Mumbai.

About information techniques Examinations Board (ISEB)

ISEB qualifications add expense to expert careers by pass of proposing both the capability and the platform for focus and greater profession building. through ISEB, BCS deliver trade-diagnosed skills that measure competence, capability and efficiency in lots of areas of IS, with the goal of raising industry requisites, promotion career construction and offering aggressive side for employers. until date, ISEB issued over one hundred fifty five,000 IT qualifications throughout entire disciplines of IT. The ISEB portfolio now offers a big variety of IT skills in lots of disciplines

The foundation certificates in application trying out offered by pass of ISEB now has twin accreditation with the ISTQB (foreign utility trying out qualifications Board) and a success candidates will derive hold of certification with both ISEB and ISTQB recognition on the groundwork stage.




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ISEBSWTINT-001 exam Dumps Source : ISEB Software Testing Intermediate

Test Code : ISEBSWTINT-001
Test title : ISEB Software Testing Intermediate
Vendor title : ISEB
: 25 real Questions

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Is It Time To Test Your novel Product's Usability? 13 Tech Experts Weigh In | killexams.com real questions and Pass4sure dumps

The novel product development cycle can subsist quite complex, especially when it comes to technology. There’s a lot of planning, testing and tinkering that must befall before a tech product is ready to hit the market, and sometimes it requires going back to the drawing board.

An essential step in this process is usability testing, which helps determine whether a product functions as expected or whether it needs additional fine tuning. But how achieve you know the privilege time to commence this testing? To find out, they asked a group of Forbes Technology Council members to participate their opinions.

1. The thought Is On Paper

You should test as early as there is a hypothesis to validate. Usability testing should start with sketches and low-fi prototypes as early as an thought is vetted within the company walls. Earlier feedback always provides extremely valuable insight, not only to usability, but likewise in gauging interest for the novel product. - Bruno Guicardi, CI&T

2. Code Is Being Written

If there is code being written, you should subsist testing usability. This usability testing not only covers your customers, but likewise your developers and any internal team member who interacts with the product or codebase. This is imperative to back your velocity lofty and beget an facile transition to focusing on usability testing for your customer without taking steps backward. - Philip Hutchins, Storj Labs Inc.

3. The First Mockup Is Ready

It’s best to start usability testing from the very beginning — from the very first wireframe and mockup you have. Otherwise, you are just assuming. Early usability testing could succor you celebrate the pass users will interact with your product and what their struggles, concerns and biggest needs would be. - Ivailо Nikolov, SiteGround

4. You occupy Users

You’re ready as soon as you occupy users. A lesson Y-Combinator taught me is that to beget something people want, you should subsist exercising two considerable things: talking to users and structure the product. They’re both important, but talking to users is the first step in testing usability. If you’re not doing it, start now, then mosey on to more formal processes when needed. - Russell Smith, Rainforest QA, Inc

5. You occupy Basic Criteria For Usability

Finding out the user group on both edges of the consumer spectrum would subsist the first key step to test readiness for usability. The usability findings matrix formed by these user groups against the mock model of the product should subsist vetted against the intermediate product outcomes to note if they surpass the 80/20 rule that is set as the gating criteria for usability. - Shafeer Badharudeen, Attinad Software

6. Tasks Are Ready For A User To Complete

Usability testing should start as soon as there are tasks for a user to complete, because the earlier that product teams can start getting feedback from users on what they are building, the better. Early testing can subsist as simple as informal “hallway” testing using paper prototypes. As the product matures, the testing can buy odds of a more formal, structured succession of tests. - Peter Mourfield, TaxSlayer

7. You’re poignant Into Beta Mode

With today’s compressed production cycle, usability testing should commence as soon as workable to ensure an effectual product launch. Once a product hits beta testing, it should retrograde to the domain with trusted clients. This provides a 360-degree test of the product’s usability and powerful insight into how the product will fare in the real world. There is no substitute for a real-world product test. - Paul Ryznar, OPS Solutions, LLC

8. You occupy A Diverse User base Willing To Test

Test with a wide variety of users. Validating the user’s taste for things fancy visual contrast, big fonts and screen reader back are captious to ensuring that entire your customers occupy a much experience. If your product supports multiple languages, there is nobody better to validate that your translations are on point than a endemic speaker. - Amy Czuchlewski, Bottle Rocket

9. You need To Test Any Of The Features

Usability testing is not a one-time event. It should subsist embedded throughout the lifecycle as necessary and applicable. There is nothing more costly or disappointing than to find that a strong, feature-rich application with kindly business logic is not intuitive. When this happens, adoption suffers. And sometimes it is too costly and too late to adjust for usability. - Mohamad Zahreddine, TrialAssure

10. You’re Approaching Your Minimum Viable Product Goals

Reid Hoffman, the founder of LinkedIn, said, “If you’re not embarrassed by the first version of your product, you’ve launched too late.” Pre-launch, teams should set the readiness bar even lower for usability testing — and itch for feedback. Define your minimum viable product goals at the outset of the design and start usability testing well before you’ve hit those goals so your assumptions can subsist challenged. - Evan Kohn, Pypestream

11. Your User Interface Is Ready

It’s considerable you build the user interface features of the product first, which enables the usability testing. entire core features need not subsist functional at this point in time; minimal features enabling the user to start interacting with the product and letting them taste the core features by mocking them helps us understand how intuitive it is to use, avoiding fixes post-launch. - Sujeeth Kanuganti, Aira Tech Corp

12. You occupy Incorporated The Needs And Wants Of A Beta Panel

Usability highly depends on your testing the market for your product. The thought is to assassinate two birds with one stone. When you find an intersect of needs and wants from a beta panel of users you are targeting, develop what will answer both customers. back your beta customers engaged, divulge and tell, iterate. subsist humble and agile every step of the pass and you will subsist ready for widespread testing. - Waije Coler, InfoTracer

13. You’re At Any Point In The Process

You should always test usability, from the first sketches made on paper to the very ultimate point of the design. There’s no “correct moment” to achieve it; everything you achieve in a design process should subsist user-oriented, which means being fully sensible of how they will interact with the product. If you’re not testing usability throughout the entire process, you’re working in the dark. - Nacho De Marco, BairesDev


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

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

This chapter is from the engage 

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 occupy been used in the IBM Rochester software development laboratory for some years with perpetual evolution and improvement, so there is ample implementation taste with them. This is considerable because although there are numerous metrics for software testing, and novel ones being proposed frequently, relatively few are supported by enough experiences of industry implementation to demonstrate their usefulness. For each metric, they dispute its purpose, data, interpretation, and use, and provide a graphic instance 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 enough details on testing-related metrics. Then they dispute some workable metrics for a special test scenario, acceptance test with respect 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 achieve you know your product is kindly enough to ship?

Because the examples are based on IBM Rochester's experiences, it would subsist useful to profile 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 effectual 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 chore 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 suggest that the data are cumulative over time and resemble an "S" shape as a result of the age of fierce test activity, causing a abrupt planned test ramp-up. For the metric to subsist 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 subsist 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 subsist able to buy action upon early indications that testing activity is falling behind. It is well known that when the schedule is under pressure, testing, especially development 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 development process has a sturdy 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 identical time, testing (development tests and independent tests) and customer validation are the key process phases with equally sturdy focus. As figure 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 facet 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 facet 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 development and independent tests:

  • Along with component test, a stress test is conducted in a big 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 mistake recovery processes.

  • The independent test group likewise conducts a software installation test, which runs from the middle of the component test until the finish 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 development teams. The stress test, the product-level test, and the network test are done by development 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 figure 10.1 shows, several early customer programs occur at the back finish of the development process:

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

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

  • Beta program with business partners

  • Customer beta program

  • Figure 10.2 is an instance of the component test metric at the finish of the test of a major release of an integrated operating system. As can subsist seen from the figure, the testing scheme is expressed in terms of a line curve, which is establish in residence before the test begins. The vacant bars indicate the cumulative number of test cases attempted and the solid bars picture the number of successful test cases. With the scheme 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 instance shows that during the rapid test ramp-up age (the abrupt slope of the curve), for some weeks the test cases attempted were slightly ahead of scheme (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 assign 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 employ a 10-point scale (10 for the most considerable test cases and 1 for the least). To track test points, the teams need to express the test scheme (amount of testing done every week) and track the week-by-week progress in terms of test points. The instance in figure 10.3 shows test point tracking for a product level test, which was underway, for a systems software. It is notable that there is always an element of subjectivity in the assignment of weights. The weights and the resulting test points should subsist determined in the test planning stage and remain unchanged during the testing process. Otherwise, the purpose of this metric will subsist compromised in the reality of schedule pressures. In software engineering, weighting and test score assignment remains an inviting zone where more research is needed. workable guidelines from such research will surely capitalize 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 likewise subsist weighted by some measurement of coverage. Coverage weighting and test score assignment consistency become increasingly considerable in balance to the number of development groups involved in a project. need of attention to tracking consistency across functional areas can result in a delusive view of the overall project's progress.

    When a scheme 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 subsist 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 scheme and plan-to-date (the eye can quickly determine if testing is ahead or behind on planned attempts and successes), it may subsist difficult to discern the exact amount of slippage. This is particularly genuine for big testing efforts, where the number of test cases is in the hundreds of thousands. For that reason, it is useful to likewise display the test status in tabular form, as in Table 10.1. The table likewise 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 loom to subsist 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 likewise 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 scheme Attempted

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

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

    In addition, this metric can subsist used for release-to-release or project-to-project comparisons, as the instance in figure 10.4 shows. For release-to-release comparisons, it is considerable to employ time units (weeks or days) before product ship (or common availability, GA) as the unit for the X-axis. By referencing the ship dates, the comparison provides a genuine status of the project in process. In figure 10.4, it can subsist 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 type of comparison, the project team can scheme ahead (even before the testing starts) to mitigate the risks.

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

    To implement this metric, the test execution scheme needs to subsist laid out in terms of the weekly target, and actual data needs to subsist tracked on a weekly basis. For wee to medium projects, such planning and tracking activities can employ common tools such as Lotus 1-2-3 or other project management tools. For big and knotty projects, a stronger tools back facility normally associated with the development environment may subsist needed. Many software tools are available for project management and attribute control, including tools for defect tracking and defect projections. Testing tools usually comprehend 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 facet is highly recommended as a touchstone drill for entire 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 occupy 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 identical overall defect rate during testing, different patterns of defect arrivals may imply different scenarios of domain quality. They recommend the following for this metric:

  • Always comprehend 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 level 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 instance 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 subsist used for the defect arrival patterns based on the total number of defects from entire test phases, and for defect arrivals for specific tests. It can subsist 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 real graph has much more information on it including upright lines to depict the key dates of the development cycle and system schedules such as ultimate novel role integration, development test completion, start of system test, and so forth. There are likewise variations of the metric: total defect arrivals, austere 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 cogent defects. The main, and the most useful, chart is the total number of defect arrivals. In their projects, they likewise comprehend a lofty severity (severity 1 and 2) defect chart and a normalized view as mainstays of tracking. The normalized defect arrival chart can eliminate 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 subsist useful. As figure 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 sober problems and should subsist investigated.

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

    When achieve the defect arrivals peak relative to time to product delivery? How does this pattern compare to previous releases? How lofty achieve they peak? achieve they decline to a low and stable level 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 level earlier before the product ship date, or one that is consistently lower than the baseline when it is inevitable that the effectiveness of testing is at least as kindly as previous testing. The tail finish 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, mention 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 subsist applied to the data to project the number of residual defects in the product. figure 10.7 shows such an example. The actual testing defect data represents the total cumulative defects removed when entire testing is complete. The fitted model curve is a Weibull distribution with the shape parameter (m) being 1.8. The projected dormant defects in the domain 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 finish date of testing and the product ship date, such as this case, the number of dormant defects represented by the section of the model curve for this time segment has to subsist 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 big number of outstanding defects during the development 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 found during the development cycle. For soft-ware organizations that occupy part teams to conduct development testing and to fix defects, defects in the backlog should subsist kept at the lowest workable level at entire times. For organizations that occupy the identical teams accountable for development testing and fixing defects, however, there are appropriate timing windows in the development cycle for which the priority of focuses may vary. While the defect backlog should subsist managed at a reasonable level at entire times, it should not subsist the highest priority during a age when making headway in functional testing is the critical-path development activity. During the prime time for development testing, the focus should subsist on test effectiveness and test execution, and defect discovery should subsist encouraged to the maximum workable extent. Focusing too early on overall defect backlog reduction may combat with these objectives. For example, the development team may subsist inclined not to open defect records. The focus during this time should subsist on the fix turnaround of the captious defects that impede test progress instead of the entire backlog. Of course, when testing is approaching completion, sturdy focus for drastic reduction in the defect backlog should buy place.

    For software development projects that build on existing systems, a big backlog of "aged" problems can develop over time. These aged defects often picture fixes or enhancements that developers believe would legitimately better the product, but which derive passed over during development due to resource or design constraints. They may likewise picture problems that occupy 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 zone of the defect backlog that warrants attention early in the development cycle, even prior to the start of development testing.

    Figure 10.8 is an instance 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 age 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 subsist fixed first is very considerable in terms of achieving early system stability. In this regard, the expertise and ownership of the development and test teams are crucial.

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

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

  • Monitor defect arrivals and dissect the problems (e.g., defect judgement analysis and Pareto analysis of problem areas of the product) to gain learning for improvement actions. achieve not artificially control defect arrivals, which is a role of test effectiveness, test progress, and the intrinsic attribute of the code (the amount of dormant defects in the code). achieve cheer 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 subsist accorded the highest priority.

  • The three metrics discussed so far are obviously related, and they should subsist viewed together. We'll approach 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 development team can likewise subsist tracked as a gauge of the "effort" side of the development equation. During product development, there is a trend toward growth as requirements and designs are fleshed out. Functions may continue to subsist added to meet late requirements or the development 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 likewise relate the measurement of total defect volume to per unit improvement or deterioration. figure 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 picture the different layers in the software system. This metric is likewise known as an indicator of scope creep. Note that lines of code is only one of the size indicators. The number of role points is another common indicator, especially in application software. They occupy likewise seen the number of bytes of recollection that the software will employ 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 level of stability is required to meet customers' needs, it is considerable that the product discharge well under stress. In software testing during the development process, the level of CPU utilization is an indicator of the system's stress.

    To ensure that its software testing is effective, the IBM Rochester software development laboratory sets CPU utilization targets for the software stress test and the system test. Stress testing starts at the middle of the component test facet and may shun into the system test time frame with the purpose of stressing the system in order to uncover dormant defects that judgement system crashes and hangs that are not easily discovered in intimate testing environments. It is conducted with a network of systems. System test is the final test facet with a customerlike environment. Test environment, workload characteristics, and CPU stress level 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 talent to subsist installed and operated in customerlike environments. These test environments focus on the total solution, including current release of the operating system, novel 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 achieve not attempt to replicate a particular customer, but picture 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 picture an interrelated set of companies that employ the IBM products to back and drive their day-to-day business activities. Test scenarios are defined to simulate the different types of end-user activities, work-flow, and business applications. They comprehend CPU-intensive applications and interaction-intensive computing. During test execution, the environment is shun as a 24-hour-a-day, 7-day-a-week (24x7) operation.

    Initially, toil items are defined to address complete solutions in the RAISE environment. From these toil items approach more minute scenario definitions. These scenarios are written to shun 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 endeavor 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 real 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 knotty 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 likewise because this represents a more realistic, customerlike setup. In driving multiple, interrelated, and concurrent activities across their network, they minister to "shake out" those hard-to-get-at dormant problems. In such a knotty environment, these types of problems minister to subsist difficult to analyze, debug, and fix, because of the layers of activities and products used. Additional endeavor 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 residence an accent on stressful, concurrent product interaction. Workload characteristics include:

  • Stressing some of the more knotty novel features of the system

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

  • Verifying that the software installation instructions are accurate and understandable and that the installation role 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 calamity and mistake 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 subsist minimum number of unplanned initial program loads (IPL, or reboot) except for maintenance requiring an IPL. The intent is to simulate an lively business and detect problems that occur only after the systems and network occupy been operat

    The data in figure 10.10 indicate 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 likewise shown.

    Figure 10.10Figure 10.10 CPU Utilization Targets for Testing Systems

    The next example, shown in figure 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 facet of the target, set at 18 CPU hours per system per day, is for the back finish of the stress test. As the figure shows, a key element of this metric, in addition to comparison of actual and target data, is release-to-release comparison. One can celebrate that the curve for release C had more data points in the early development cycle, which were at higher CPU utilization levels. This is because pretest runs were conducted prior to availability of the novel release content. For entire 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 utensil 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 subsist 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 subsist re-IPLed (rebooted). For software tests whose purpose is to better the stability of the system, they need to ensure that the system is stressed and testing is conducted effectively to uncover dormant defects that would lead to system crashes and hangs, or in common 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 report the endeavor aspect and the outcome aspect respectively, of the effectiveness of the test.

    Figure 10.12 shows the system crashes and hangs metric for the identical three releases shown in figure 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 utensil produces a screen prompt and requests information about the ultimate system crash or hang. The tester can ignore the prompt temporarily, but it will reappear regularly after a inevitable time until the questions are answered. Information elicited via this utensil includes test system, network ID, tester name, IPL code and judgement (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 title of the product. The IPL judgement 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 likewise subsist used to beget 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 suggest Time to Unplanned IPL

    Mean time to failure (MTTF), or suggest time between failures (MTBF), are the touchstone measurements of reliability. In software reliability literature, this metric and various models associated with it occupy been discussed extensively. Predominantly, the discussions and employ 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 subsist 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 entire failures and minute time-related data (Note: Failures are different from defects or faults; a single defect can judgement multiple failures and in different machines) in commercial projects, and the value and recur on investment of such tracking.

    System crashes and hangs (unplanned IPLs) are the more austere 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 employ suggest 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 development cycle is workable but will not subsist as meaningful because entire the components of the system cannot subsist addressed collectively until the final system test. The formula to reckon 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 shun hours W = Weighting factor I = Number of weekly (unique) unplanned IPLs (due to software failures)

    Basically the formula takes the total number of CPU shun 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 derive the weighted MTI number, if weighting is desired. For example, if the total CPU shun hours from entire 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 subsist 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 buy the outcome from the prior weeks into account so that at the finish of the system test (with a duration of 10 weeks), the MTI represents an entire system test statement. It is the practitioner's conclusion whether to employ a weighting factor or how to distribute the weights heuristically. Deciding factors may comprehend type of products and systems under test, test cycle duration, and how the test age is planned and managed.

    Figure 10.13 is an instance 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 privilege side is MTI and on the left side is the number of unplanned IPLs. Inside the chart, the shaded areas picture 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 scheme 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 subsist enough burn-in time for the system to reach the MTI target. Because this need 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 suggest Time to Unplanned IPL Metric

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

  • Extending test duration and/or adding resources

  • Providing for a more exhaustive regression test age 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 common criteria that any problem that will impede the overall progress of the project or that will occupy significant impact on customer's business (if not fixed) belongs to such a list. The tracking normally starts at the middle of the component test facet when a captious problem meeting by the project management team (with representatives from entire functional areas) takes residence once a week. When it gets closer to system test and product delivery time, the focus intensifies and daily meetings buy 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 minister to subsist problems related to installation, system stability, security, data corruption, and so forth. entire problems on the list must subsist resolved before product delivery.


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