Tuesday, October 17, 2006
Thursday, September 21, 2006
List of Test Case Management Tools
Here is a list of Test Case Management Tools. These tools help organize and execute groups of test cases, also called test suites. Some tools are for automated tests, some for manual tests, and some handle both.
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Saturday, August 12, 2006
Selecting the best defect tracking system
The following is a general comparison overview of Bug Tracking Tools. It is focused particularly on the technical aspect. The aim is to help you to select the best tracking system that meets your requirement. Click on the link for a more detailed comparison between Bugzero, Bugzilla, and Gnats.
System Architecture:
- Many old bug or defect tracking systems are client server based. You need install the server, and each user need install the client software. If external users were involved, it could be problematic because of issues like firewall etc. Also, it is not always feasible to install client software.
- Newer systems are more likely web browser based and no client software need to be installed (except a browser). A web-based bug tracking system is especially attractive if your users are located in different locations and are connected through the internet.
- For a web-based bug or defect tracking system, make sure it supports the browsers your users are using. Be aware that many systems support only IE.
Server Operating System:
- Most commercial bug tracking systems are Windows based. In such a case, it is likely that it requires an NT/2000/XP server and a SQL Server database. Note that, a Windows XP Professional may not be sufficient, instead, a server may be required.
- Most free bug or defect tracking systems are Linux/Unix based, and may not work as well on Windows. It may also require more technical skills to install and setup the system.
- When people say their system is cross-platform, you need make sure they meant the server. Only a very few bug tracking systems are really cross-platform (with the same code base). Some vendors claim to support multiple OS, but they have completely independent versions for each OS and that results in higher costs for the vendor and therefore higher price for the end users.
Backend Database:
- Most bug or defect tracking systems require a backend database, but a few are file based. In the latter case, make sure it scales well. If someone tells you that a file based system is better than a database, think twice.
- For Windows based systems, database selection may be limited to only Access and SQL Server. On the other hand, some free systems may lock you into just one database, notably MySQL. Only a very few bug tracking systems are really cross database systems.
- Be aware of any bug tracking software that uses non-standard proprietary databases. They cannot be better than the public, commonly used database systems.
Language Support:
- Many bug tracking systems do not support localization, particually, Asian langauges. Note that, it involves the web interface, the data, and the email notification.
- If you do need localization, you should find a system that can do that easily.
Web Server:
- For Windows based bug tracking systems, most likely it requires IIS as the web server.
- For Java-based bug tracking systems, a Servlet or J2EE server is most likely required. There are many high quality servers you can download for free.
Programming Language:
- Most of the bug tracking systems are written in either c/c++, or perl/php, or Java.
- Depending on your IT environment and skill set, the programming language may be relevant in selecting your system. For example, if you are developing Java software, it may make sense to use a Java based bug tracking system.
Version Control Integration:
- Some bug tracking systems have the capability of integrating with source control systems, such as CVS, Source Safe, etc.
- Be aware of the limitations, and make sure it does the things you want.
Installation and Configuration:
- A bug tracking system is not a desktop application and it rarely works out-of-the-box. It is not uncommon to spend a few hours to setup such a system, and then more time to customize it.
- However, if you need only a lightweight bug tracking system, a heavy, complex, can-do-everything system is certainly a over kill and it may do more harm than good.
Maintenance and Support:
- A bug tracking tool is not a super complex software system, but from time to time you may need technical support. As you certainly know, in most cases, the error messages from these systems are always cryptic, and you won't be able to solve the problem on your own.
- How is the error handled in a tool is far more important than you might think. You as the administrator may want select a tool that you feel comfortable to work with.
- When support is needed, it is always urgent to you, but not necessary to the vendor. Before you purchase the software, you should ask what is the response time for support.
Features:
- Simple is the key here. The system must be simple that people like to use it, but not so complex that people avoid to use it. You might not want to deploy a tool that requires serious end user training. It is really not the initial training, rather the on-going support needed from your end users that you should be concerned with.
- Yet it should be flexible and configurable enough to satisfy your business needs. If you select a tool that cannot do whatever you intend it to do, then what is the use of it?
Cost of Ownship:
- The initial cost of a bug tracking system varies from free to tens of thousands of dollars. But be aware that this is not the same as the total cost of the ownership. Some free systems charge a hefty consulting fee for support and you may end up paying much more than you planned.
- You should select a bug tracking system based on your needs, not just the price. If you know what you are doing and do not need commercial support, go for a free one if it meets your requirement.
- However, if you unfortunately selected a bad one, you better get out of it as soon as possible, because the longer you keep it, the more moeny and time you will have to spend on it.
Friday, July 21, 2006
Unit Testing with Mock Objects
Unit testing is a fundamental practice in Extreme Programming, but most non-trivial code is difficult to test in isolation. It is hard to avoid writing test suites that are complex, incomplete, and difficult to maintain and interpret. Using Mock Objects for unit testing improves both domain code and test suites. They allow unit tests to be written for everything, simplify test structure, and avoid polluting domain code with testing infrastructure.
You need to make sure that you test one feature at a time, and you want to be notified as soon as any problem occurs. Normal unit testing is hard because you are trying to test the code from outside.
There is a technique called Mock Objects in which we replace domain code with dummy
implementations that emulate real code. These Mock Objects are passed to the target domain code which they test from inside, also termed as Endo-Testing. This practice is similar to writing code stubs with two interesting differences: we test at a finer level of granularity than is usual, and we use our tests and stubs to drive the development of our production code.
Developing unit tests with Mock Objects leads to stronger tests and to better structure of both domain and test code. Unit tests written with Mock Objects have a regular format that gives the development team a common vocabulary. We believe that code should be written to make it easy to test, and have found that Mock Objects is a good technique to achieve this.
An essential aspect of unit testing is to test one feature at time; you need to know exactly what you are testing and where any problems are. Test code should communicate its intent as simply and clearly as possible. This can be difficult if a test has to set up domain state or the domain code causes side effects. Worse, the domain code might not even expose the features to allow you to set the state necessary for a test.
A Mock Object is a substitute implementation to emulate or instrument other domain code. It should be simpler than the real code, not duplicate its implementation, and allow you to set up private state to aid in testing. The emphasis in mock implementations is on absolute simplicity, rather than completeness. For example, a mock collection class might always return the same results from an index method, regardless of the actual parameters.
Mock Objects are not just stubs
As a technique, Mock Objects is very close to Server Stubs. The main concerns about using Server Stubs are: that stubs can be too hard to write, that the cost of developing and maintaining stubs can be too high, that dependencies between stubs can be cyclic, and that switching between stub and production code can be risky.
Why use Mock Objects?
An important aspect of Extreme Programming is not to commit to infrastructure before you have to. For example, we might wish to write functionality without committing to a particular database. Until a choice is made, we can write a mock class that provides the minimum behaviour that we would expect from our database. This means that we can continue writing the tests for our application code without waiting for a working database. The mock code also gives us an initial definition of the functionality we will require from the database.
Unit tests, as distinct from functional tests, should exercise a single piece of functionality. A unit test that depends on complex system state can be difficult to set up, especially as the rest of the system develops. Mock Objects avoid such problems by providing a lightweight emulation of the required system state. Furthermore, the setup of complex state is localised to one Mock Object instead of scattered throughout many unit tests.
Some unit tests need to test conditions that are very difficult to reproduce. For example, to test server failures we can write a Mock Object that implements the local proxy for the server.
Domain objects often fail some time after an error occurs, which is one reason that debugging can be so difficult. With tests that query the state of a domain object, all the assertions are made together after the domain code has executed. This makes it difficult to isolate the exact point at which a failure occurred. One of the authors experienced such problems during the development of a financial pricing library. The unit tests compared sets of results after each calculation had finished. Each failure required considerable tracing to isolate its cause, and it was difficult to test for intermediate values without breaking encapsulation.
Limitations of Mock Objects
As with any unit testing, there is always a risk that a Mock Object might contain errors, for example returning values in degrees rather than radians. Similarly, unit testing will not catch failures that arise from interactions between components. For example, the individual calculations for a complex mathematical formula might be within valid tolerances, and so pass their unit tests, but the cumulative errors might be unacceptable. This is why functional tests are still necessary, even with good unit tests. Extreme Programming reduces, but does not eliminate, such risks with practices such as Pair Programming and Continuous Integration.
Mock Objects reduce this risk further by the simplicity of their implementations.
In some cases it can be hard to create Mock Objects to represent types in a complex external library. The most difficult aspect is usually the discovery of values and structures for parameters that are passed into the domain code. In an event-based system, the object that represents an event might be the root of a graph of objects, all of which need mocking up for the domain code to work. This process can be costly and sometimes must be weighed against the benefit of having the unit tests. However, when only a small part of a library needs to be stubbed out, Mock Objects is a useful technique for doing so.
One important point that we have learned from trying to retrofit Mock Objects is that, in
statically typed languages, libraries must define their APIs in terms of interfaces rather than classes so that clients of the library can use such techniques.
You need to make sure that you test one feature at a time, and you want to be notified as soon as any problem occurs. Normal unit testing is hard because you are trying to test the code from outside.
There is a technique called Mock Objects in which we replace domain code with dummy
implementations that emulate real code. These Mock Objects are passed to the target domain code which they test from inside, also termed as Endo-Testing. This practice is similar to writing code stubs with two interesting differences: we test at a finer level of granularity than is usual, and we use our tests and stubs to drive the development of our production code.
Developing unit tests with Mock Objects leads to stronger tests and to better structure of both domain and test code. Unit tests written with Mock Objects have a regular format that gives the development team a common vocabulary. We believe that code should be written to make it easy to test, and have found that Mock Objects is a good technique to achieve this.
An essential aspect of unit testing is to test one feature at time; you need to know exactly what you are testing and where any problems are. Test code should communicate its intent as simply and clearly as possible. This can be difficult if a test has to set up domain state or the domain code causes side effects. Worse, the domain code might not even expose the features to allow you to set the state necessary for a test.
A Mock Object is a substitute implementation to emulate or instrument other domain code. It should be simpler than the real code, not duplicate its implementation, and allow you to set up private state to aid in testing. The emphasis in mock implementations is on absolute simplicity, rather than completeness. For example, a mock collection class might always return the same results from an index method, regardless of the actual parameters.
Mock Objects are not just stubs
As a technique, Mock Objects is very close to Server Stubs. The main concerns about using Server Stubs are: that stubs can be too hard to write, that the cost of developing and maintaining stubs can be too high, that dependencies between stubs can be cyclic, and that switching between stub and production code can be risky.
Why use Mock Objects?
An important aspect of Extreme Programming is not to commit to infrastructure before you have to. For example, we might wish to write functionality without committing to a particular database. Until a choice is made, we can write a mock class that provides the minimum behaviour that we would expect from our database. This means that we can continue writing the tests for our application code without waiting for a working database. The mock code also gives us an initial definition of the functionality we will require from the database.
Unit tests, as distinct from functional tests, should exercise a single piece of functionality. A unit test that depends on complex system state can be difficult to set up, especially as the rest of the system develops. Mock Objects avoid such problems by providing a lightweight emulation of the required system state. Furthermore, the setup of complex state is localised to one Mock Object instead of scattered throughout many unit tests.
Some unit tests need to test conditions that are very difficult to reproduce. For example, to test server failures we can write a Mock Object that implements the local proxy for the server.
Domain objects often fail some time after an error occurs, which is one reason that debugging can be so difficult. With tests that query the state of a domain object, all the assertions are made together after the domain code has executed. This makes it difficult to isolate the exact point at which a failure occurred. One of the authors experienced such problems during the development of a financial pricing library. The unit tests compared sets of results after each calculation had finished. Each failure required considerable tracing to isolate its cause, and it was difficult to test for intermediate values without breaking encapsulation.
Limitations of Mock Objects
As with any unit testing, there is always a risk that a Mock Object might contain errors, for example returning values in degrees rather than radians. Similarly, unit testing will not catch failures that arise from interactions between components. For example, the individual calculations for a complex mathematical formula might be within valid tolerances, and so pass their unit tests, but the cumulative errors might be unacceptable. This is why functional tests are still necessary, even with good unit tests. Extreme Programming reduces, but does not eliminate, such risks with practices such as Pair Programming and Continuous Integration.
Mock Objects reduce this risk further by the simplicity of their implementations.
In some cases it can be hard to create Mock Objects to represent types in a complex external library. The most difficult aspect is usually the discovery of values and structures for parameters that are passed into the domain code. In an event-based system, the object that represents an event might be the root of a graph of objects, all of which need mocking up for the domain code to work. This process can be costly and sometimes must be weighed against the benefit of having the unit tests. However, when only a small part of a library needs to be stubbed out, Mock Objects is a useful technique for doing so.
One important point that we have learned from trying to retrofit Mock Objects is that, in
statically typed languages, libraries must define their APIs in terms of interfaces rather than classes so that clients of the library can use such techniques.
Saturday, June 17, 2006
Checklist for Test Preparation
Listed below are questions/suggestions for systematically planning and preparing software testing.
- Have you planned for an overall testing schedule and the personnel required, and associated training requirements?
- Have the test team members been given assignments?
- Have you established test plans and test procedures for
- module testing,
- integration testing,
- system testing, and
- acceptance testing?
- Have you designed at least one black-box test case for each system function?
- Have you designed test cases for verifying quality objectives/factors (e.g. reliability, maintainability, etc.)?
- Have you designed test cases for verifying resource objectives?
- Have you defined test cases for performance tests, boundary tests, and usability tests?
- Have you designed test cases for stress tests (intentional attempts to break system)?
- Have you designed test cases with special input values (e.g. empty files)?
- Have you designed test cases with default input values?
- Have you described how traceability of testing to requirements is to be demonstrated (e.g. references to the specified functions and requirements)?
- Do all test cases agree with the specification of the function or requirement to be tested?
- Have you sufficiently considered error cases? Have you designed test cases for invalid and unexpected input conditions as well as valid conditions?
- Have you defined test cases for white-box-testing (structural tests)?
- Have you stated the level of coverage to be achieved by structural tests?
- Have you unambiguously provided test input data and expected test results or expected messages for each test case?
- Have you documented the purpose of and the capability demonstrated by each test case?
- Is it possible to meet and to measure all test objectives defined (e.g. test coverage)?
- Have you defined the test environment and tools needed for executing the software test?
- Have you described the hardware configuration an resources needed to implement the designed test cases?
- Have you described the software configuration needed to implement the designed test cases?
- Have you described the way in which tests are to be recorded?
- Have you defined criteria for evaluating the test results?
- Have you determined the criteria on which the completion of the test will be judged?
- Have you considered requirements for regression testing?
Wednesday, May 31, 2006
Task-Based Software Testing
Introduction
There is a plethora of software testing techniques available to a development team. A survey by Zhu, identified over 200 unit testing techniques. However, for the services’ operational test agencies, there has been a continuing, unanswered question of how to test software’s impact on a system’s mission effectiveness. I propose a task-based approach as part of an integrated test strategy in an effort to answer this long-standing question.
Why Test?
From a speech by Lloyd K. Mosemann II, at the time the Deputy Assistant Secretary for the Air Force (Communications, Computers, and Support Systems), a customer’s concerns are:
They want systems that are on-time, within budget, that satisfy user requirements, and are reliable.
A report from the National Research Council refines the latter two concerns in his statement by presenting two broad objectives for operational testing:
to help certify, through significance testing, that a system’s performance satisfies its requirements as specified in the ORD and related documents, and
to identify any serious deficiencies in the system design that need correction before full rate production
Following the path from the system level to software, these two reasons are consistent with the two primary reasons for testing software or software intensive systems. Stated generically, these are:
test for defects so they can be fixed, and
test for confidence in the software
The literature often refers to these as “debug” and “operational” testing, respectively. Debug testing is usually conducted using a combination of functional test techniques and structural test techniques. The goal is to locate defects in the most cost-effective manner and correct the defects, ensuring the performance satisfies the user requirements. Operational testing is based on the expected usage profile for a system. The goal is to estimate the confidence in a system, ensuring the system is reliable for its intended use.
Task-Based Testing
Task-based testing, as I define it here, is a variation on operational testing. It uses current DoD doctrine and policy to build a framework for designing tests. The particular techniques are not new, rather it leverages commonly accepted techniques by placing them within the context of current DoD operational and acquisition strategies.
Task Analysis
Task-based testing, as the name implies, uses task analysis. Within the DoD, this begins with the Uniform Joint Task List and, in the case of the Air Force, is closely aligned with the Air Force Task List (AFTL). The AFTL “...provides a comprehensive framework for all of the tasks that the Air Force performs.” Through a series of hierarchical task analyses, each unit within the service creates a Mission Essential Task List (METL). The Mission Essential Tasks (METs) are “...only those tasks that represent the indispensable tasks to that particular organization.”
METLs, however, only describe “what” needs to be done, not “how” or “who.” Further task decomposition identifies the system(s) and people required to carry out a mission essential task. Another level of decomposition results in the system tasks (i.e. functions) a system must provide. This is, naturally, the level in which developers and testers are most interested. From a tester’s perspective, this framework identifies the most important functions to test by correlating functions against the mission essential tasks a system is designed to support.
This is distinctly different from the typical functional testing or “test-to-spec” approach where each function or specification carries equal importance. Ideally, there should be no function or specification which does not contribute to a task, but in reality there are often requirements, specifications, and capabilities which do not or minimally support a mission essential task. Using task analysis, one identifies those functions impacting the successful completion of mission essential tasks and highlights them for testing.
Operational Profiles
The above process alone has great benefit in identifying what functions are the most important to test. However, the task analysis above only identifies the mission essential tasks and functions, not their frequency of use. Greater utility can be gained by combining the mission essential tasks with an operational profile an estimate of the relative frequency of inputs that represent field use. This has several benefits:
“...offers a basis for reliability assessment, so that the developer can have not only the assurance of having tried to improve the software, but also has an estimate of the reliability actually achieved.”
“...provides a common base for communicating with the developers about the intended use of the system and how it will be evaluated.”
“When testing schedules and budgets are tightly constrained, this design yields the highest practical reliability because if failures are seen they would be the high frequency failures.”
The first benefit has the advantage of applying statistical techniques, both in the design of tests and in the analysis of resulting data. Software reliability estimation methods such as those are available to estimate both the expected field reliability and the rate of growth in reliability. This directly supports an answer to the long-standing question about software’s mpact on a system’s mission effectiveness as well as answering Mr. Mosemann II’s fourth concern a customer has (is it reliable).
Operational profiles are criticized as being difficult to develop. However, as part of its current operations and acquisition strategy, the DoD inherently develops an operational profile. At higher levels, this is reflected in such documents as the Analysis of Alternatives (AOA), the Operational Requirements Document (ORD), Operations Plans, Concept of Operations (CONOPS), etc. Closer to the tester’s realm is the interaction between the user and the developer which the current acquisition strategy encourages. The tester can act as a facilitator in helping the user refine his or her needs while providing insight to the developer on expected use. This highlights the second benefit above the communication between the user, developer, and tester.
The third benefit is certainly of interest in today’s environment of shrinking budgets and manpower, shorter schedules (spiral acquisition), and greater demands on a system. Despite years of improvement in the software development process, one still sees systems which have gone through intensive debug testing (statement coverage, branch coverage, etc.) and “test-to-spec,” but still fail to satisfy the customer’s concerns as stated by Mr. Mosemann II. By involving a customer early in the process to develop an operational profile, the most needed functions to support a task will be developed and tested first, increasing the likelihood of satisfying the customer’s four concerns.
Task-Based Software Testing
Task-based software testing, as defined herein, is the combination of a task analysis and an operational profile. The task analysis helps partition the input domain into mission essential tasks and the system functions which support them. Operational profiles, based on these tasks, are developed to further focus the testing effort.
Integrated Testing
Operational testing is not without its weaknesses. As a rather obvious example of this, one can raise the question, “What about a critical feature that is seldom executed?” Operational testing, or task-based testing as defined herein, does not address such questions well. Debug testing, with the explicit goal of locating defects in a cost-effective manner, is more suited to this.
Debug Testing
Debug testing is “...directed at finding as many bugs as possible, by either sampling all situations likely to produce failures (e.g., methods informed by code coverage or specification criteria), or concentrating on those that are considered most likely to produce failures (e.g., stress testing or boundary testing methods).” survey of unit testing methods are examples of debug testing methods. These include such techniques as statement testing, branch testing, basis path testing, etc. Typically associated with these methods are some criteria based on coverage, thus they are sometimes referred to as coverage methods. Debug testing is based on a tester’s hypothesis of the likely types and locations of bugs. Consequently, the effectiveness of this method depends heavily on whether the tester’s assumptions are correct.
If a developer and/or tester has a process in place to correctly identify the potential types and locations of bugs, then debug testing may be very effective at finding bugs. If a “standard” or “blind” approach is used, such as statement testing for its own sake, the testing effort may be ineffectual and wasted. A subtle hazard of debug testing is that it may uncover many failures, but in the process wastes test and repair effort without notably improving the software because the failures occur at a negligible rate during field use.
Integration of Test Methods
Historically, a system’s developer relied on debug testing (which includes functional or “test-to-spec” testing). Testing with the perspective of how the system would by employed was not seen until an operational test agency (OTA) became involved. Even on the occasions when developmental test took on an operational flavor, this is viewed as too late in the process. This historical approach to testing amplifies the weaknesses of both operational and debug testing. I propose that task-based software testing be accelerated to a much earlier point in the acquisition process. This has the potential of countering each respective method’s weaknesses with the other’s strengths. This view is supported by the current philosophy in the test community, to develop a combined test force spanning contractor, developmental, and operational test (CT/DT/OT).
Summary
Task-based software evaluation is a combination of demonstrated, existing methods (task analysis and operational testing). Its strength lies in matching well with the DoD’s current operational strategy of mission essential tasks and the acquisition community’s goal to deliver operational capability quickly. By integrating task-based software testing with existing debug testing, the risk of meeting the customer’s four concerns (on-time, within budget, satisfies requirements, and is reliable) can be reduced.
Friday, April 28, 2006
Test Efficiency Vs Test Effectiveness
S. No | Test Efficiency | Test Effectiveness |
1 | Test efficiency= internal in the organization how much resources were consumed how much of these resources were utilized | Test effectiveness = how much the customer's requirements are satisfied by the system how well the customer specifications are achieved by the system how much effort is put in developing the system |
2 | Number of test cases executed divided by unit of time (generally per hour). | Number of defects found divided by number of test cases executed. |
3 | Test efficiency = (total number of defects found in unit+integration+system) / (total number of defects found in unit+integration+system+User acceptance testing) | Test effectiveness = (total number of defects injected +total number of defect found) / (total number of defect escaped)* 100 |
4 | Test Efficiency: Test the amount of code and testing resources required by a program to perfirm a function | Test Effectivness: It judge the Effect of the test enviornment on the application |
5 | Testing Efficiency = (No. of defects Resolved / Total No. of Defects Submitted)* 100 | Test Effectiveness = Loss due to problems / Total resources processed by the system |
6 | Test Efficiency is the rate of bugs found by the tester to the total bugs found. Let me explain it more clearly: When the build is sent to the customer side people for the testing (Alpha and Beta Testing), the customer side people also find some bugs. Test Efficiency = A/(A+B) Here A= Number of bugs found by the tester B= Number of bugs found by the custome side people This test efficiency should always be greater than 90% |
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