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Software QA and Testing Less-Frequently-Asked-Questions

This section of Softwareqatest.com is for those who have some experience in the software development world and already have a grasp of QA and testing basics. (For information on QA and Testing basics, see Softwareqatest.com sections FAQ 1 and FAQ 2).

Items in this section address some 'beyond-the-basics' questions, along with some discussion of possible answers.

Some of the questions have no definitive answers, some are questions more often dealt with by upper management (if they are dealt with at all), and some have more to do with psychology than technology. For these reasons they tend to be questions that are less frequently asked, even though they are important questions.

Additional items will be added over time. Some of the questions shown were submitted previously by readers; suggestions for additional topics are welcome.


Why is it sometimes hard for organizations to get serious about quality assurance?
Solving problems is a high-visibility process; preventing problems is low-visibility. This is illustrated by an old parable:
In ancient China there was a family of healers, one of whom was known throughout the land and employed as a physician to a great lord. The physician was asked which of his family was the most skillful healer. He replied,
"I tend to the sick and dying with drastic and dramatic treatments, and on occasion someone is cured and my name gets out among the lords."
"My elder brother cures sickness when it just begins to take root, and his skills are known among the local peasants and neighbors."
"My eldest brother is able to sense the spirit of sickness and eradicate it before it takes form. His name is unknown outside our home."
This is a problem in any business, but it's a particularly difficult problem in the software industry. Software quality problems are often not as readily apparent as they might be in the case of an industry with more physical products, such as auto manufacturing or home construction.

Additionally: Many organizations are able to determine who is skilled at fixing problems, and then reward such people. However, determining who has a talent for preventing problems in the first place, and figuring out how to incentivize such behavior, is a significant challenge.

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Who is responsible for risk management?
Risk management means the actions taken to avoid things going wrong on a software development project, things that might negatively impact the scope, quality, timeliness, or cost of a project. This is, of course, a shared responsibility among everyone involved in a project. However, there needs to be a 'buck stops here' person who can consider the relevant tradeoffs when decisions are required, and who can ensure that everyone is handling their risk management responsibilities.

It is not unusual for the term 'risk management' to never come up at all in a software organization or project. If it does come up, it's often assumed to be the responsibility of QA or test personnel. Or there may be a 'risks' or 'issues' section of a project, QA, or test plan, and it's assumed that this means that risk management has taken place.

The issues here are similar to those for the LFAQ question "Who should decide when software is ready to be released?" It's generally NOT a good idea for a test lead, test manager, or QA manager to be the 'buck stops here' person for risk management. Typically QA/Test personnel or managers are not managers of developers, analysts, designers and many other project personnel, and so it would be difficult for them to ensure that everyone on a project is handling their risk management responsibilities. Additionally, knowledge of all the considerations that go into risk management mitigation and tradeoff decisions is rarely the province of QA/Test personnel or managers. Based on these factors, the project manager is usually the most appropriate 'buck stops here' risk management person. QA/Test personnel can, however, provide input to the project manager. Such input could include analysis of quality-related risks, risk monitoring, process adherence reporting, defect reporting, and other information.

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Who should decide when software is ready to be released?
In many projects this depends on the release criteria for the software. Such criteria are often in turn based on the decision to end testing, discussed in FAQ #2 item "How can it be known when to stop testing?" Unfortunately, for any but the simplest software projects, it is nearly impossible to adequately specify useful criteria without a significant amount of assumptions and subjectivity. For example, if the release criteria are based on passing a certain set of tests, there is likely an assumption that the tests have adequately addressed all appropriate software risks. For most software projects, this would of course be impossible without enormous expense, so this assumption would be a large leap of faith. Additionally, since most software projects involve a balance of quality, timeliness, and cost, testing alone cannot address how to balance all three of these competing factors when release decisions are needed.

A typical approach is for a lead tester or QA or Test manager to be the release decision maker. This again involves significant assumptions - such as an assumption that the test manager understands the spectrum of considerations that are important in determining whether software quality is 'sufficient' for release, or the assumption that quality does not have to be balanced with timeliness and cost. In many organizations, 'sufficient quality' is not well defined, is extremely subjective, may have never been usefully discussed, or may vary from project to project or even from day to day.

Release criteria considerations can include deadlines, sales goals, business/market/competitive considerations, business segment quality norms, legal requirements, technical and programming considerations, end-user expectations, internal budgets, impacts on other organization projects or goals, and a variety of other factors. Knowledge of all these factors is often shared among a number of personnel in a large organization, such as the project manager, director, customer service manager, technical lead or manager, marketing manager, QA manager, etc. In smaller organizations or projects it may be appropriate for one person to be knowledgeable in all these areas, but that person is typically a project manager, not a test lead or QA manager.

For these reasons, it's generally not a good idea for a test lead, test manager, or QA manager to decide when software is ready to be released. Their responsibility should be to provide input to the appropriate person or group that makes a release decision. For small organizations and projects that person could be a product manager, a project manager, or similar manager. For larger organizations and projects, release decisions might be made by a committee of personnel with sufficient collective knowledge of the relevant considerations.

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What can be done if requirements are changing continuously?
Although changing requirements are considered an acceptable, or even expected, aspect of agile software development processes, this is a common problem for organizations where there are expectations that requirements can be pre-determined and remain stable. If these expectations are within a project context where they are reasonable, here are some approaches:

If this is a continuing problem, and the expectation that requirements can be pre-determined and remain stable is NOT reasonable, it may be a good idea to figure out why the expectations are not aligned with reality, and to refactor an organization's or project's software development process to take this into account. It may be appropriate to consider agile development approaches (or to refactor the existing agile development approach).

Also see What is Agile Software Development and how does it impact testing? in FAQ #2.

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What if the application has functionality that wasn't in the requirements?
It may take serious effort to determine if an application has significant unexpected or hidden functionality, and it could indicate deeper problems in the software development process. If the functionality isn't necessary to the purpose of the application, it should be removed, as it may have unknown impacts or dependencies that were not taken into account by the designer or the customer. (If the functionality is minor and low risk then no action may be necessary.) If not removed, information will be needed to determine risks and to determine any added testing needs or regression testing needs. Management should be made aware of any significant added risks as a result of the unexpected functionality.

This problem is a standard aspect of projects that include COTS (Commercial Off-The-Shelf) software or modified COTS software. The COTS part of the project will typically have a large amount of functionality that is not included in project requirements, or may be simply undetermined. Depending on the situation, it may be appropriate to perform in-depth analysis of the COTS software and work closely with the end user to determine which pre-existing COTS functionality is important and which functionality may interact with or be affected by the non-COTS aspects of the project. A significant regression testing effort may be needed (again, depending on the situation), and automated regression testing may be useful.

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How can Software QA processes be implemented without reducing productivity?
By implementing QA processes slowly over time, using consensus to reach agreement on processes, focusing on processes that align tightly with organizational goals, and adjusting/experimenting/refactoring as an organization matures, productivity can be improved instead of stifled. Problem prevention will lessen the need for problem detection, panics and burn-out will decrease, and there will be improved focus and less wasted effort. At the same time, attempts should be made to keep processes simple and efficient, avoid a 'Process Police' mentality, minimize paperwork, promote computer-based processes and automated tracking and reporting, minimize time required in meetings, and promote training as part of the QA process. However, no one - especially talented technical types - likes rules or bureaucracy, and in the short run things may slow down a bit. A typical scenario would be that more days of planning, reviews, and inspections will be needed, but less time will be required for late-night bug-fixing and handling of irate customers.

Other possibilities include incremental self-managed team approaches such as 'Kaizen' methods of continuous process improvement, the Deming-Shewhart Plan-Do-Check-Act cycle, and others.

(See the Softwareqatest.com Bookstore section's 'Software QA', 'Software Engineering', and 'Project Management' categories for useful books with more information.)

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What if an organization is growing so fast that fixed QA processes are impossible?
This is a common problem in the software industry, especially in new technology areas. There is generally no easy solution in this situation. One approach is:

Depending on the growth rate, it is possible that incremental self-managed team approaches may be applicable, such as 'Kaizen' methods of continuous process improvement, or the Deming-Shewhart Plan-Do-Check-Act cycle, and others.

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Will automated testing tools make testing easier?

     code analyzers - monitor code complexity, adherence to
                      standards, etc.

     coverage analyzers - these tools check which parts of the
                      code have been exercised by a test, and may
                      be oriented to code statement coverage,
                      condition coverage, path coverage, etc.

     memory analyzers - such as bounds-checkers and leak detectors.

     load/performance test tools - for testing client/server
                      and web applications under various load
                      levels.

     web test tools - to check that links are valid, HTML code
                      usage is correct, client-side and
                      server-side programs work, a web site's
                      interactions are secure.
                                         
     other tools - for test case management, BDT (behavior-driven testing),
                      documentation, management, bug reporting, and configuration
                      management, file and database comparisons, screen 
                      captures, security testing, macro recorders, etc.

Test automation is, of course, possible without COTS tools. Many successful automation efforts utilize open source tools, or custom automation software that is targeted for specific projects, specific software applications, or a specific organization's software development environment. In test-driven agile software development environments, automated tests are often built into the software during (or preceding) coding of the application.

See the Automation section of the 'Other Resources' page for other resources on test automation.

See the Softwareqatest.com Bookstore section on Software Test Automation for useful books with more information.

See the 'Tools' section for test tool listings and the 'Web Tools' section for web site testing tools.

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What's the best way to choose a test automation tool?
It's easy to get caught up in enthusiasm for the 'silver bullet' of test automation, where the dream is that a single mouse click can initialize thorough unattended testing of an entire software application, bugs will be automatically reported, and easy-to-understand summary reports will be waiting in the manager's in-box in the morning.

Although that may in fact be possible in some situations, it is not the way things generally play out.

In manual testing, the test engineer exercises software functionality to determine if the software is behaving in an expected way. This means that the tester must be able to judge what the expected outcome of a test should be, such as expected data outputs, screen messages, changes in the appearance of a User Interface, XML files, database changes, etc. In an automated test, the computer does not have human-like 'judgement' capabilities to determine whether or not a test outcome was correct. This means there must be a mechanism by which the computer can do an automatic comparison between actual and expected results for every automated test scenario and unambiguously make a pass or fail determination. This factor may require a significant change in the entire approach to testing, since in manual testing a human is involved and can: For those new to test automation, it might be a good idea to do some reading or training first. There are a variety of ways to go about doing this; some example approaches are: As in anything else, proper planning and analysis are critical to success in choosing and utilizing an automated test tool. Choosing a test tool just for the purpose of 'automating testing' is not useful; useful purposes might include: testing more thoroughly, testing in ways that were not previously feasible via manual methods (such as load testing), testing faster, enabling continuous integration processes, or reducing excessively tedious manual testing. Automated testing rarely enables savings in the cost of testing, although it may result in software lifecycle savings (or increased sales) just as with any other quality-related initiative.

With the proper background and understanding of test automation, the following considerations can be helpful in choosing a test tool (automated testing will not necessarily resolve them, they are only considerations for automation potential):

Taking into account the testing needs determined by analysis of these considerations and other appropriate factors, the types of desired test tools can be determined. For each type of test tool (such as functional test tool, load test tool, etc.) the choices can be further narrowed based on the characteristics of the software application. The relevant characteristics will depend, of course, on the situation and the type of test tool and other factors. Such characteristics could include the operating system, GUI components, development languages, web server type, etc. Other factors affecting a choice could include experience level and capabilities of test personnel, advantages/disadvantages in developing a custom automated test tool, tool costs, tool quality and ease of use, usefulness of the tool on other projects, etc.

Once a short list of potential test tools is selected, several can be utilized on a trial basis for a final determination. Any expensive test tool should be thoroughly analyzed during its trial period to ensure that it is appropriate and that it's capabilities and limitations are well understood. This may require significant time or training, but the alternative is to take a major risk of a mistaken investment (in terms of time, resources, and/or purchase price).

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How can it be determined if a test environment is appropriate?
This is a difficult question in that it typically involves tradeoffs between 'better' test environments and cost. The ultimate situation would be a collection of test environments that mimic exactly all possible hardware, software, network, data, and usage characteristics of the expected live environments in which the software will be used. For many software applications, this would involve a nearly infinite number of variations, and would clearly be impossible. And for new software applications, it may also be impossible to predict all the variations in environments in which the application will run. For very large, complex systems, duplication of a 'live' type of environment may be prohibitively expensive.

In reality judgements must be made as to which characteristics of a software application environment are important, and test environments can be selected on that basis after taking into account time, budget, and logistical constraints. Such judgements are preferably made by those who have the most appropriate technical knowledge and experience, along with an understanding of risks and constraints.

For smaller or low risk projects, an informal approach is common, but for larger or higher risk projects (in terms of money, property, or lives) a more formalized process involving multiple personnel and significant effort and expense may be appropriate.

In some situations it may be possible to mitigate the need for maintenance of large numbers of varied test environments. One approach might be to coordinate internal testing with beta testing efforts. Another possible mitigation approach is to provide built-in automated tests that run automatically upon installation of the application by end-users. These tests might then automatically report back information, via the internet, about the application environment and problems encountered. Another possibility is the use of virtual environments instead of physical test environments, using such tools as VMWare or VirtualBox.

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What's the best approach to software test estimation?
There is no simple answer for this. The 'best approach' is highly dependent on the particular organization and project and the experience of the personnel involved.

For example, given two software projects of similar complexity and size, the appropriate test effort for one project might be very large if it was for life-critical medical equipment software, but might be much smaller for the other project if it was for a low-cost computer game. A test estimation approach that only considered size and complexity might be appropriate for one project but not for the other.

Following are some approaches to consider:

Implicit Risk Context Approach:
A typical approach to test estimation is for a project manager or QA manager to implicitly use risk context, in combination with past personal experiences in the organization, to choose a level of resources to allocate to testing. In many organizations, the 'risk context' is assumed to be similar from one project to the next, so there is no explicit consideration of risk context. (Risk context might include factors such as the organization's typical software quality levels, the software's intended use, the experience level of developers and testers, etc.) This is essentially an intuitive guess based on experience.

Metrics-Based Approach:
A useful approach is to track past experience of an organization's various projects and the associated test effort that worked well for projects. Once there is a set of data covering characteristics for a reasonable number of projects, then this 'past experience' information can be used for future test project planning. (Determining and collecting useful project metrics over time can be an extremely difficult task.) For each particular new project, the 'expected' required test time can be adjusted based on whatever metrics or other information is available, such as function point count, number of external system interfaces, unit testing done by developers, risk levels of the project, etc. In the end, this is essentially 'judgement based on documented experience', and is not easy to do successfully.

Test Work Breakdown Approach:
Another common approach is to decompose the expected testing tasks into a collection of small tasks for which estimates can, at least in theory, be made with reasonable accuracy. This of course assumes that an accurate and predictable breakdown of testing tasks and their estimated effort is feasible. In many large projects, this is not the case. For example, if a large number of bugs are being found in a project, this will add to the time required for testing, retesting, bug analysis and reporting. It will also add to the time required for development, and if development schedules and efforts do not go as planned, this will further impact testing.

Iterative Approach:
In this approach for large test efforts, an initial rough testing estimate is made. Once testing begins, a more refined estimate is made after a small percentage (e.g., 1%) of the first estimate's work is done. At this point testers have obtained additional test project knowledge and a better understanding of issues, general software quality, and risk. Test plans and schedules can be refactored if necessary and a new estimate provided. Then a yet-more-refined estimate is made after a somewhat larger percentage (e.g., 2%) of the new work estimate is done. Repeat the cycle as necessary/appropriate.

Percentage-of-Development Approach:
Some organizations utilize a quick estimation method for testing based on the estimated programming effort. For example, if a project is estimated to require 1000 hours of programming effort, and the organization normally finds that a 40% ratio for testing is appropriate, then an estimate of 400 hours for testing would be used. This approach may or may not be useful depending on the project-to-project variations in risk, personnel, types of applications, levels of complexity, etc.

Successful test estimation is a challenge for most organizations, since few can accurately estimate software project development efforts, much less the testing effort of a project. It is also difficult to attempt testing estimates without first having detailed information about a project, including detailed requirements, the organization's experience with similar projects in the past, and an understanding of what should be included in a 'testing' estimation for a project (functional testing? unit testing? reviews? inspections? load testing? security testing?)

With agile software development approaches, test effort estimations may be unnecessary if pure test-driven development is utilized. However, it is not uncommon to have a mix of some automated positive-type unit tests, along with some type of separate manual or automated functional testing. In general, agile-based projects by their nature will not be heavily dependent on large one-shot testing efforts, since they emphasize the construction of releasable software in short iteration cycles. Test estimates are often focused on individual 'stories' and the testing associated with each of these. These smaller multiple test effort estimates may not be as difficult to estimate and the impact of inaccurate estimates will be less severe, and expectations are that estimates will improve with each sprint.

For an interesting view of the problem of test estimation, see the comments on Martin Fowler's web site indicating that, for many large systems, "testing and debugging is impossible to schedule" (see sixth paragraph in the article).

Additionally, see Vinton Cerf's October 2012 ACM article 'Where is the Science in Computer Science?' in which he asks about bugs: 'Do we know how long it will take to find and fix them? Do we know how many new bugs our fixes will create?' and makes it clear that the answer is usually 'No'. Also see an April 2013 article by Ron Jeffries in which he states 'Estimation is very difficult, perhaps impossible, and often misused.'

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