Archive for the ‘STEM’ Category

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Quality injection – Scientific validation of requirements

Monday, August 9th, 2010
domain knowledge. One of our Japanese customer threw a challenge – “How
can you use HBT/STEM to scientifically validate requirements without knowing
the domain deeply?” .
The core aspect of HBT is to hypothesize potential defect types that prove that
they do not exist. These are identified by keeping in mind the end users and
the technology used to construct the system. So how do you apply this to
validate a pre-code artifact?
We commenced by identifying the various stakeholders for requirement
document and then identified key cleanliness attributes. These cleanliness
attributes if met would imply that the requirements was indeed clean. We were
excited by this. We then moved and identified potential defect types that would
impede these cleanliness attributes/criteria.
Lo behold, the problem was cracked and we then identified the various types
and the corresponding evaluation scenarios for validating the requirements/
architecture document. We came up with THIRTY+ defect types that required
about 10+ types tests conducted over TEN quality levels with a total of SIXTY
FIVE major requirement evaluation scenarios to validate a requirement.
What we came up is not yet-another-inspection-process that is dependent on
domain knowledge, but a simple & scientific approach consisting a set of
requirement evaluation scenarios that could be applied with low domain skill to
ensure that the requirement/architecture can indeed be validated rapidly and
effectively. These ensure that the requirement document is useful to the various
stakeholders over the software lifecycle and does indeed satisfy the intended
application/product attributes.
We now have a unique solution “Clean Requirements Solution” that is an
adaptation of HBT to validate requirements.

Validating early stage pre-code artifacts like requirement document is challenging. This is typically done by rigorous inspection and requires deep domain knowledge. One of our Japanese customer threw a challenge – “How can you use HBT/STEM to scientifically validate requirements without knowing the domain deeply?” .

The core aspect of HBT is to hypothesize potential defect types that prove that they do not exist. These are identified by keeping in mind the end users and the technology used to construct the system. So how do you apply this to validate a pre-code artifact?

We commenced by identifying the various stakeholders for requirement document and then identified key cleanliness attributes. These cleanliness attributes if met would imply that the requirements was indeed clean. We were excited by this. We then moved and identified potential defect types that would impede these cleanliness attributes/criteria.

Lo behold, the problem was cracked and we then identified the various types and the corresponding evaluation scenarios for validating the requirements/ architecture document. We came up with THIRTY+ defect types that required about 10+ types tests conducted over TEN quality levels with a total of SIXTY FIVE major requirement evaluation scenarios to validate a requirement.

What we came up is not yet-another-inspection-process that is dependent on domain knowledge, but a simple & scientific approach consisting a set of requirement evaluation scenarios that could be applied with low domain skill to ensure that the requirement/architecture can indeed be validated rapidly and effectively. These ensure that the requirement document is useful to the various stakeholders over the software lifecycle and does indeed satisfy the intended application/product attributes.

We now have a unique solution “Clean Requirements Solution” that is an adaptation of HBT to validate requirements.

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How many “negative test cases” should there be?

Thursday, April 29th, 2010

Before we answer the question let us ensure that we have common understanding of what is positive or negative test cases. In HBT, positive test cases are those whose input values are valid, while a negative test case is one with at least one of the test inputs (i.e. Test data) being incorrect (i.e. out of specification).

The objective of positive test cases is “conformance” while the objective of negative test cases is “robustness”. If a majority of test case are positive, then it implies that we are primarily interested in conformance i.e. ensuring the system handles correct inputs well. This we know is not sufficient, as accidental incorrect inputs should not result in unexpected possibly risky/dangerous behaviour. Hence we need test cases that are indeed “negative”.

So, coming back to the question, what is a good enough distribution of positive and negative test cases? Any quick answer like 75% should not be trusted as they have no basis. So, how do we answer this question? Step back now and look at the number of inputs for a given test case, the clue is there. For example at a lower level of testing, where say we are validating a screen, the number of inputs may be many as the screen may be populated. As we go up the testing level, e.g. Testing a feature that uses a few screens, the test data is not the various individual inputs on the screen, but aggregate data (think like a record) and these may be fewer in number compared to the earlier levels.

Having understood that the number of test data (or inputs) at lower levels is far higher than those at the higher levels, it is only logical to conclude that the number of negative test cases at lower levels. Now how much should that be?  To answer his finally without resorting to magic (!), let us illustrate with simple example. If there are 5 inputs and each input has six possible values (3 positive i.e. valid and 3 negative i.e. invalid) then using simple combinatorial math, we can see that there be (minimally) 3*5=15 negative test cases and (minimally) 5 positive test cases. In this case the 15/(15+5)= (75%) of test cases are negative.

In closing, understanding the number of inputs and clear understanding of what an input at a testing level is (The STEM Core concept “Input granularity principle” of HBT methodology helps in understanding as what an input at a level is) it is possible to quickly estimate the minimal number of negative test cases. This is very useful in quickly ascertaining whether the test cases are conformance and robustness oriented.

This is covered in “HBT.10: Effective review of test cases”, a title in our HBT Series of workshops

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Requirements traceability is “Necessary but not sufficient”

Thursday, April 29th, 2010

When asked about “how do you know that your test cases are adequate?”, the typical answer is Requirement Traceability Matrix(RTM) has been generated and that each requirement does indeed have test cases.

Is this logic strong enough? Unfortunately NO! Why? Assume that each requirement had just one test case. This implies that we have good RTM i.e. each requirement has been covered. What we do know is that could there additional test cases for some of the requirements? So RTM is a necessary condition but NOT a sufficient condition.

So, what does it take to be sufficient? If we had a clear notion of types of defects that could affect the customer experience and then mapped these to test cases, we have Fault Traceability Matrix (FTM as proposed by HBT). This allows us to be sure that our test cases can indeed detect those defects that will impact customer experience.

Note that in HBT potential defects types are mapped to the Cleanliness Criteria derived earlier. Cleanliness criteria are those that have to be met to ensure that customer has a good experience with the system.

This is covered in “HBT.10 : Effective review of test cases”, a title in our HBT Series of workshops.

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Effectiveness and Efficiency of test cases

Thursday, April 29th, 2010

Given that we have set of test cases, we would like then them to be effective. What does “effective” mean? Effectiveness of test cases is the ability of the test cases to be able to detect (or uncover) the defects that can affect the customer experience.  So a clear understanding of what *types of defects* are we looking for and a mapping of the test cases to these test cases would enable a scientific way of assessing effectiveness.

What is efficiency? It is ensuring that we execute the test cases in as short a time as possible with optimal effort and no more.  Understanding (1) the priority or business importance of test cases, (2) knowing what test cases to execute in what part of the lifecycle (3) clear segregation of test cases by various types of tests and levels enables to optimize testing and become efficient.

This is covered in our HBT Series of workshop “HBT.10 : Effective review of test cases”.

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Ensuring testable non-functional requirements

Thursday, April 29th, 2010

Non-functional requirements are notoriously non-testable! By this, we mean it is more typical that non-functional requirements are fuzzy/less-clear. In a simplistic form “The system should be robust” is non-testable i.e. It is definitely not clear as how to validate this!

Rather than identifying non-functional requirements and describing them, it is suggested that we look at each requirement and partition these into functional and non-functional aspects and probe into the key attributes to be satisfied for the requirement. For attribute, GQM (Goal-Question-Metric) of core concept of STEM enables deriving metric(s) to ensure that each attribute is indeed testable.  Later the various similar attributes across all the requirements can be aggregated to create the system-wide non-functional requirement.

In this manner non-functional requirements are clearer and testable.

This is covered in “HBT.2: Rapid Understanding of customer expectations”, a title in our HBT Series of workshops.

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Rapidly understanding the usage profile

Thursday, April 29th, 2010

Understanding the rate and number of transactions probably on a real system is critical to ensure that the system is designed well and later sized and deployed well. Good understanding of the business domain is seen as a key enabler to arrive at the usage profile.

Operational profiling (A STEM core concept) is a scientific way to quickly arrive at a real life profile of usage. Good understanding of this concept alleviates the problem of lack of deep domain knowledge to understand the usage profile. This core concept consists of these key aspects:

  1. Mode – Represents a time period of usage e.g. End of month, where the usage patterns are distinctive and different.
  2. Key operations (features/requirements) used
  3. Types of end users associated with the key features/requirements
  4. Number of end users for each type of users
  5. Rate of arrival of transactions

In short, for a given mode, identify the end users types and their key operations and then identify the number of users for each type of user and then identify the rate of arrival of transaction. Employing this core concept allows us to think better and ask specific questions to understand the marketplace and the usage profile in a typical and worst-case scenario.

This allows us to get a better understanding of the usage and helps in identifying business risks and derive an effective strategy.

This is covered in “HBT.2 : Rapid Understanding of customer expectations”, a title in our HBT Series of workshops.

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Landscaping – A STEM Core concept to understanding system & expectations

Thursday, April 29th, 2010

Understanding a system is non-linear process i.e. we have derive a lot of questions that are interconnected and then seek answers individually and then connect the various answers.

Landscaping is a core concept in STEM (STAG Test Engineering Method) that powers HBT (Hypothesis based testing) that lists the various aspects related to the system, customer, marketplace, technology and forces one to connect these concepts. What emanates is like an interesting web of information (aka Mindmap) that enables you to come up with intelligent questions and there enable rapid understanding.

For example, connecting marketplace, with requirements and end-users, one can arrive at the question: “What are the various markets that we are planning to deploy our system and in these markets, who are the various kinds of end user types and what does each one from the system?

To quote a specific instance, a two-liner requirement spawned 40+ questions rapidly, that when clarified allowed us to understand the requirement in about an hour. In fact, this uncovered issues in the product being built, as certain aspects of the requirements were completely missed by the developers in their implementation. It is to be noted that our ability to identify questions and therefore understand were not due our domain skills, it was purely due to the application of HBT methodology powered by the defect detection technology(STEM) to the problem at hand.

This is covered in “HBT.2: Rapid Understanding of customer expectations”, a title in our HBT Series of workshops.

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Launching two new HBT series of workshops in MAY 2010

Wednesday, April 21st, 2010

STAG is launching two new workshops in the “HBT Series” in May 2010. We thank our participants of Robust Test Design workshop conducted in Chennai, for suggesting that we come with a new workshop for “How to understand customer expectations rapidly” . Thank you!

The workshops are:

  1. Rapid understanding of customer expectations  on May 27, 2010 at Bangalore and on June 7 at Chennai.
  2. Effective review of test cases  on May 28, 2010 at Bangalore and June 8, 2010 at Chennai.

Workshop details:

Rapid understanding of customer expectations (1-day workshop)

Objective: How to rapidly understand expectations/requirements of the software to be validated in a scientific manner.

Target audience: Test manager, Project manager, Test lead, Test Engineers

To test effectively, estimate correctly, a good understanding of the software/application is very important. The act of understanding is a high maturity skill requiring multiple skills. Domain knowledge is seen as a critical enabler to good understanding. Good documentation of the requirements is also a key ingredient. In real life however, the available documentation of requirements/specifications always lacks the details required for effective testing, and is typically not in sync with the software/system being built. Therefore the test staff with their domain knowledge is expected to come up with good questions and clarify the missing elements and understand the intended behaviors. This is easier said that done, as deep domain knowledge is typically a scarce commodity.

This workshop takes a scientific approach to “act of understanding the intentions or expectations” by identifying key elements required of any requirement/specification and identifying a personal process powered by scientific concepts to ensure that we rapidly understand the intentions and identify the missing elements.

The first two stages of Hypothesis-based testing(HBT) methodology focuses on “Understanding expectations” and “Understand context”, this is powered by the “Business Value Understanding” discipline of STEM, the underlying defect detection technology that powers HBT. This discipline employs seven scientific concepts to enable that the various aspects to understand the expectations can indeed be done in a scientific manner.

This has been successfully used by STAG in its engagements to rapidly identify questions to understand expectations. To quote a specific instance, a two-liner requirement spawned 40+ questions rapidly, that when clarified allowed us to understand the requirement in about hour. In fact, this uncovered issues in the product being built,  as certain aspects of the requirements were completely missed by the developers in their implementation. It is to be noted that our ability to identify questions and therefore understand were not due our domain skills, it was purely due to the application of HBT methodology powered by the defect detection technology(STEM) to the problem at hand.

The topics covered in this workshop are:

  • How to create the big picture and get a good overall view (Landscaping)
  • Identifying end users and their needs
  • Identifying business requirements and  corresponding technical requirements
  • Identifying critical attributes and ensuring that they are testable
  • Understanding the usage patterns/operational profile
  • Identifying business risks and prioritization
  • Understanding intended behaviors for designing test scenarios/cases
  • Formulating cleanliness criteria

The participants will be able to create a User type list, Requirement list, Operational profile, Interaction matrix, Cleanliness criteria  and key questions to understanding expectations at the end of this workshop.

The delivery style will be application oriented, an application will be used to illustrate the concepts and the process of doing.

Each participant will be given a HBT cookbook (NEW!) in addition to the workshop slide set and application case study.

Effective review of test cases (1-day workshop)

Objective: How to assess effectiveness, completeness, consistency and future automation-ability of test cases.

Target audience: Test leads and Test engineers

Test effectiveness is a function of the quantity and quality of test scenarios/cases. The difficult  aspect is assessing if the designed scenarios/cases are indeed adequate. As always, a deep domain and technical knowledge is seen as a critical aspect to effectively review the test scenarios/cases. The challenging part is that deep domain/technical skills is always in short supply.

This workshop teaches a scientific approach to assess the quality of test scenarios/cases by applying a goal centered to testing – “What types of defects should I detect”?. Commencing with identification of potential defect types that will impact the customer experience, the designed scenarios/cases are analyzed for fault coverage in addition to requirements coverage. HBT powered by STEM has a clear structure for effective test scenarios/cases (TS/TC) and this is the basis for assessment of the scenarios/cases. The STEM Test Case Architecture (STEM-TCA) architecture slices the scenarios/cases is multiple ways and allows one to see the gaps in the designed scenarios/cases. For example STEM-TCA requires TS/TC to be segregated by potential defect types and then by quality levels and then by test types, by conformance vs robustness, by importance and other attributes. This approach enables a scientific enquiry process and allows one to assess rapidly and effectively without totally relying on the domain knowledge.

STAG has used this successfully in its boutique service offering of “Test case re-engineering” in its engagements to increase coverage(i.e. defect finding ability)  significantly with its customers. One such interesting work is listed in our blog “Re-architecting  test assets increases test coverage by 250%.” In addition, STAG  has used these concepts to assess completeness of TS/TC for its Japanese customers in their “Diagnostics &  control” solution offering.

At the end of workshop you will able to review the designed scenarios/cases rapidly & effectively enabling you to “produce better bait” to catch the fish! I.e defect defects. The information content of test scenarios/cases plays a vital part of in embarking on successful automation to improve efficiencies. Moving from effectiveness, the workshop will be also enable assessing the efficiency aspect of the testing I.e how can I order and deliver design  assets that will enable faster testing. This is addressed in the assessment of TS/TC on the  automation-fitness aspect.

The topics covered in this workshop are:

  • Understanding the goal of TS/TC i.e. what types of defects should we detect?
  • Assessing basic completeness using RTM(Requirement traceability matrix)
  • Understanding the caveat of RTM i.e. it is necessary but sufficient enough
  • Creating fault traceability matrix
  • Understanding the STEM-TCA
  • Identifying information needed for assessment
  • The personal assessment process for effectiveness and efficiency of TS/TC
  • Understanding the distribution of conformation-oriented(positive) vs robustness (negative)oriented over the various levels of test
  • Limitations of black box techniques
  • What information related to internal aspects of the software do I need to know i.e. how to use white box techniques effectively
  • Metrics that are useful to substantiate the assessment like Test breadth, Test depth and Test granularity

The participants will be able to a create clear assessment report with appropriate metrics   to judge the efficacy and efficiency aspects  at the end of this  workshop.

The delivery style will be application oriented implying test scenarios/case of an real-life application will be used to illustrate the concepts and the process of doing.

Each participant will be given a HBT cookbook (NEW!) in addition to the workshop slide set and application case study.

Both these workshops are limited to a maximum of 25 participants on first-come basis. Email learning at stagsoftware dot com for registration or for more information. We are excited about launching these two unique workshops and look forward to interacting with you.

Click here to download the HBT Series of Workshops brochure.

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“The quality race” – STEM wins

Thursday, April 15th, 2010

A large German major with a mature QA practice is seeking new ways to improve its test practice. It all starts with a talk  delivered at this company on “The Science & Engineering of Effective Testing” to its senior management staff and test practitioners in the company. We are amazed at the interest in this topic (75+ people attend the talk) and the enthusiastic response – we are deeply humbled.

A few weeks after this, the management decides to experiment with STEM-based approach to testing. They identify about twenty five people (a small subset of their QA team) to be trained on the new way of testing. We are delighted and conduct a 5-day workshop with intense application orientation, to enable them to understand STEM. The company then decides to conduct a bold experiment- a pilot to evaluate STEM powered approach to testing vis-a-vis their way of testing. They identify a product that is in use for a few years with consumers across the world. They decide to have two five-member identical teams consisting of similar mix of experience levels of people, each given a timeframe of one month to evaluate the new release of this product. These two teams are  kept apart to ensure a controlled experiment and the countdown starts. We wait with bated breath…

The month is slow for us, but it flies for the two teams. Enormous data has been generated and the management analyses them thoroughly to spot the winner. A month later we are called by the senior management. We are sweating, have we won? A few minutes later, it is clear that STEM is a winner. The STEM powered team has designed 3x test cases compared to the non-STEM team and  uncovered 2x number of defects! The icing on the cake is that the couple of defects uncovered by the STEM powered team are “residual defects” i.e. they have been latent in the product for over a year (Remember Minesweeper game on Windows) and one of them corrupts the entire data in the database. Now the discussion steers to effort/time analysis – Does application of STEM require more effort/time? The team has conclusive evidence that it is not significant, implying STEM has enabled them to think better,  not work longer or more.

What enabled the STEM powered team to win the “Race of quality” ? The answers are given by the team itself, and we are delighted, as we have believed in them, and have seen results when we implement them. The top three reasons are: (1) The notion of Potential Defect Types (PDT) is powerful as it forces the team to hypothesize what can go wrong and enable them to setup a purposeful quality goal (2) PDT forces a thorough understanding of the customer expectations and the intended behavior of the product (3) PDT ensures that test design creates adequate test cases, thus eliminating defect escapes and paving the way for robust software.

The STAG team is delighted as their customer acknowledges the effectiveness of the STEM based approach. The team is convinced that STEM powered approach is a winner and is raring to run the marathon, with the customer also cheering them to win!

A heartfelt “Thank you” to the STEM powered team and the innovation-centered Senior Management of the company.

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Re-architecting test assets increases test coverage by 250%

Thursday, April 15th, 2010

This company is an innovative online banking solution provider, having three major products catering to over 100 top financial institutions(FI) of the world including the top five FI in the world. They have a very successful product line, growing rapidly, with major releases approximately every year, incorporating new features to cater to the various needs of the market place. As the code base evolved, the test assets were also modified to reflect the changed product. The challenge faced was that the most of the test cases were passing and the rate of uncovering new defects was low.

The product became huge and the company decided to re-architect the product in order to enable rapid feature addition with low risk. That is when the company decided to re-look at its test assets and re-architect the same to increase the test coverage, improve defect finding ability and ensure that the test assets were future-proof. It had about 8000 test cases then.

We were chartered to analyze the existing test cases for completeness and modifiability and re-architect the same after filling the gaps and to ensure that the future test cases were easily pluggable. Applying STEM, we performed a thorough assessment of the existing test assets and discovered holes in the same. Using the STEM Test Case Architecture (STEM-TCA), we re-engineered the test cases by firstly grouping them into features, then by levels of tests and segregating into various types of tests and then finally by separating into positive and negative test cases. During this process of fitment of existing of test cases into the STEM-TCA, we uncovered quite a few holes. These were filled by STAG by designing 5000 test cases additionally. Not only did the STEM-TCA increase the test coverage by uncovering the missing test cases, it also provided a sharper visibility of the quality as the test cases were well organized by specific defect types. This improved the test coverage by about 250% and the technical management staff were confident about the adequacy of test assets and were also convinced about its future upgradeability and maintainability.

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