A Survey of Software Testing Tools for Computational Science

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  • 1. RAL-TR-2007-010 A Survey of Software Testing Tools for Computational Science L.S. Chin, D.J. Worth, and C. Greenough June 29, 2007 Abstract This report presents a summary of information gathered in considering software testing prac- tices for Computational Science and Engineering. It includes an overview of software testing, and provides a survey of tools currently available to assist in implementing testing solutions for scientific applications written in Fortran. Keywords: software testing, software quality, verification, validation, Fortran Email: L.S.Chin@rl.ac.uk, D.J.Worth@rl.ac.uk, or C.Greenough@rl.ac.uk Reports can be obtained from www.softeng.cse.clrc.ac.uk Software Engineering Group Computational Science & Engineering Department Rutherford Appleton Laboratory Harwell Science and Innovation Campus Didcot Oxfordshire OX11 0QX
  • 2. c Science and Technology Facilites Council Enquires about the copyright, reproduction and requests for additional copies of this report should be address to: Library and Information Services STFC Rutherford Appleton Laboratory Harwell Science and Innovation Campus Didcot Oxfordshire OX11 0QX Tel: +44 (0)1235 445384 Fax: +44 (0)1235 446403 Email:library@rl.ac.uk STFC e-reports are available online at: http://epubs.cclrc.ac.uk Neither the Council nor the Laboratory accept any responsibility for loss or damage arising from the use of information contained in any of their reports or in any communication about their tests or investigations
  • 3. Contents 1 Introduction 1 2 Software Engineering Support Programme 2 3 Overview of Software Testing 3 3.1 Stages of Software Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3.1.1 Design phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3.1.2 Testing phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3.1.3 Maintenance phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.1.4 Implementation phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.2 Test Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.2.1 The Black Box approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.2.2 The White Box approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.3 Deciding on a strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 4 Available Tools 8 4.1 Testing Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 4.1.1 pFUnit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 4.1.2 fUnit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4.1.3 DejaGNU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4.1.4 QMTest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 4.1.5 Cleanscape Grayboxx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 4.2 Capture and Playback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.2.1 AutoExpect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.2.2 TestWorks CAPBAK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.3 Output validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.3.1 TextTest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.3.2 ndiff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.3.3 Toldiff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.3.4 numdiff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.4 Test Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.4.1 gcov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.4.2 Polyhedron plusFort - CVRANAL . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.4.3 FCAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.4.4 Cleanscape Grayboxx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.4.5 TestWorks/TCAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.4.6 LDRA Testbed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.4.7 McCabe IQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.5 Test Management and Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.5.1 RTH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.5.2 TestLink . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.5.3 QaTraq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.5.4 AutoTest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.5.5 STAF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.6 Build Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.6.1 BuildBot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.6.2 test-AutoBuild . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.6.3 Parabuild . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.6.4 CruiseControl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.6.5 BuildForge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.6.6 AEGIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 5 Further reading 20 References 21 i
  • 4. 1 Introduction Software has a long history of being used by the scientific community as a vehicle for performing world class research. These software are usually written by a variety of developers, and often evolve over time to incorporate new algorithms, models or features, or are refactored to take advantage of different programming paradigms and cutting-edge technology. Test suites that accompany these software play a crucial role in checking that the software functions correctly and produces the expected results. A good set of tests serves as a safety net for developers, ensuring that the software remains valid and internally consistent as changes are made. Additionally, these tests allow for independent verification by the end users thus building confidence in the software. Since most scientific software (and their test suites) are developed by domain experts rather then Software Engineers, there is a tendency for emphasis to be on the represented model or calculation. Tests are therefore designed around checking for acceptable results rather than discovering when or how the software might fail. Subsequently, this may lead to a situation where inadequate sets of tests lull developers into a false state of confidences. A 100% passing rate for a test suite that exercises only 30% of the program code could easily mislead developers and end-users. Similarly, tests that produce correct results for a small subset of input may lead to incorrect assumptions that the results will remain valid for all other input. This report documents the first steps in determining strategies for adopting high-payoff software testing practices within the scientific software development. We look at well established methodologies practiced by the Software Engineering community, as well as software testing tools that can accelerate the process of building, running, and managing these solutions. Due to the predominance of Fortran among scientific software projects, it is difficult for developers to take advantage of many of the available testing tools designed mainly for the general software engineering community that has long shied away from Fortran. Chapter 3, which draws heavily from the 2004 edition of the text by Myers1 [2], provides a broad overview of software testing concepts and methodology used in Software Engineering. Chapter 4 presents a survey of software tools that can potentially be used to implement testing solutions for scientific software. This list is quite extensive, and serves as a starting point for further evaluation efforts. This report is one of the outputs of the Software Engineering Support Programme (SESP). 1 The original book, published in 1979, is often regarded as a seminal work on software testing. 1
  • 5. 2 Software Engineering Support Programme The Software Engineering Support Programme (SESP) (http://www.sesp.cse.clrc.ac.uk/) is an EP- SRC support activity to provide and encourage the use of up-to-date software engineering techniques and tools in software development within computational science and engineering. The main goals of this SES Programme are: • accelerate the introduction and widespread use of high-payoff software engineering practices and technology by identifying, evaluating, and maturing promising or underused technology and prac- tices; • maintain a long-term competency in software engineering and technology transition; • enable the UK academic community to make measured improvements in their software engineering practices by working with them directly; • encourage the adoption and sustained use of standards of excellence for software engineering prac- tice; • foster collaborations with other groups, in the UK, Europe and the US, that have an interest in the applications of advanced software engineering techniques in computational science. These goals will improve the level of software engineering practice within UK computational science research groups. As a result, the software they develop will be of a higher quality; more easily developed and maintained; more easily re-used within the community and be computationally more efficient. The main thrust of the programme is to gather together processes and tools that will help improve software engineering in computational science. This can be characterised by the Technology Watch, Assessment and Evaluations process. Although the software engineering community has various very formally defined processes of software assessment and evaluation a rather more pragmatic approach has been defined for SESP. Technology Watch – In each elements of the SESP information is gathered on a regular basis and a rolling update made to a Technology Report that would be made available to the community through the SESP Web site. Assessments – The starting point of selecting a tool for use in anger is through paper assessment using a basic requirements document. The detail of the assessment would clearly depend on the area being addressed but a there will always be a collection of fundamental requirements such as operating systems, supported languages etc. These paper assessments can identify tools for practical evaluation and much of the material developed in the paper assessments added to the technology watch reports. Evaluations – Through the assessment, various tools will be selected for more direct evaluation. They would be used in a realistic context either by SESP staff or those involved in the CCP and HEC programmes and their usefulness and effectiveness documented. Although in general the evaluations would not be placed on critical paths within the CCP or HEC activities, these programmes provide a considerable number of representative software packages that can be made the subject of an evaluation. The evaluations would lead to detailed reports and if successful the deployment of the tool or practice within the main stream. At present the two major foci for the programme are on software quality assurance and transformation of legacy software. 2
  • 6. 3 Overview of Software Testing Software testing involves more than just running a program to see whether it works. A single test run reveals nothing about the program other than the obvious fact that it can yield results for a particular set of inputs. Software testing should be treated as an investigative exercise; one which systematically uncovers different classes of errors within the code while demonstrating that the software behaves as expected. The developers’ concept of the definition and objectives of software testing plays a major role in determining the efficacy of the activity. It influences the developers’ decision on what should be tested, and judgement on what is considered a ‘successful’ test. For example, if the definition “Software Testing is a process of proving that a program is bug free” were adopted, there would be a natural tendency for developers to subconsciously write fewer or less ‘destructive’ test cases with lower probabilities of breaking the program. Furthermore, the objective that this definition implies is practically impossible to achieve. It takes only one failed test to prove the existence of bugs, but requires an infinite amount of test cases to prove otherwise. Tests can only find defects, not prove that there are none. A similarly delusive definition would be “Software Testing is a process of proving that a program performs its intended functions”. This line of thinking often leads to test cases that focuses only on program behaviour that is inherently expected . However, programs that perform the right functions when given a controlled set of inputs are still erroneous if they also produce unwanted side effects or fail when given unexpected inputs. A complete test should check for both expected and unexpected behaviours, using valid as well as invalid inputs. Myers [2] aptly defines software testing as “a process of executing a program with the intention of finding errors”. Using the analogy of a medical diagnosis, a successful investigation is one that seeks and discovers a problem, rather than one that reveals nothing and provides a false sense of well-being. Based on this definition, we establish that a good set of test cases should be one that has a high chance of uncovering previously unknown errors, while a successful test run is one that discovers these errors. In order to detect all possible errors within a program, exhaustive testing is required to exercise all possible input and logical execution paths. Except for very trivial programs, this is economically unfeasible if not impossible. Therefore, a practical goal for software testing would be to maximise the probability of finding errors using a finite number of test cases, performed in minimum time with minimum effort. Section 3.2 presents several test design strategies that can be used to work towards this goal. 3.1 Stages of Software Testing Figure 1 presents an illustration of the different phases of software development with a list of activities that make up each phase. It is an extension of the V-model, and includes an additional Maintain loop to cater for iterative software development models (e.g. evolutionary prototyping, staged delivery, etc.) that may be more relevant to scientific application development. The diagram is admittedly over-elaborate as it attempts to be all-encompassing; it is not meant to describe wholly the development process of a particular software project, but instead provide a corre- lation between different activities that represent the building blocks of software development projects. Developers may wish to consider only those activities relevant to their project, and from the diagram, determine where the different software testing stages could be applied within their software development process. 3.1.1 Design phase The design phase represents a stream of activities where the software specifications are defined, starting from a high level specification of requirements down to the detailed description of the implementation. At each stage, an associated document is produced as well as the test criteria which reflect the requirements specified in the document. If it is feasible, for instance in the case of acceptance tests based on user requirements, the actual test cases should be written at this stage Test criteria drawn up at the design phase would be based on an objective view of specifications, resulting in a more complete and accurate representation of the requirements. 3
  • 7. Figure 1: Extended V-Model which includes a Maintenance phase 3.1.2 Testing phase The testing phase is made up of the different stages of testing, which reflects a bottom-up correspondence with the levels in which software is designed and built. Unit Testing : Testing a code module in isolation, ensuring that it works correctly as specified by the detailed design. Good unit tests assist in future refactoring of code, since they give assurance that the modified code still works as expected and can therefore be included into the project. Integration Testing : Testing of communication and interaction between different code modules that are to be integrated. Integration tests are defined based on the architectural design of the system, and provide confidence that all modules can work together to achieve the functionalities specified in the design. Code Coverage Analysis : Determining the level of coverage for previous tests. If the level does not meet a predefined threshold, the test cases should be extended until a satisfactory coverage level is attained. Since the coverage of test cases depends on the actual code implementation, coverage has to be re-evaluated whenever code changes to ensure that coverage level is maintained. Test coverage will be discussed further in section 3.2.2. System Testing : Testing of system level requirements as stipulated by the software requirements specification. This might include tests for performance, interoperability, portability, usability, installability, etc. Acceptance Testing : Testing of the final product against user requirements specification. ‘User ’ may refer to actual 4
  • 8. end-users using the program, or in the case of prototypes or novel applications, the developers that define what they are attempting to achieve. There is a flow leading down from each test level back to the implementation phase. This represents the fact that failed tests are followed by an implementation of a fix, and a re-execution of all tests. This form of regression testing attempts to detect any new bugs that might have been introduced when the code was modified. While it does seem like a lot of work, there are tools that aid in managing and automating tests. Test management and build management tools not only make it easier to run tests, they also provide other useful functionalities such as e-mail notifications, report generation, and defect tracking. A list of these tools are provided in section 4.5 and 4.6. 3.1.3 Maintenance phase The maintenance phase begins the moment the software is released. As feedback and bug reports are received, updates to the code are planned, and the required changes reflected in the documentation and test cases. The process then flows back into the Implementation phase followed by the Testing phase before the new version of the code is released. 3.1.4 Implementation phase The implementation phase acts the link between all other phases, and represents all activities involved in translating ideas and design into a working program. Activities that make up this phase are not limited to the writing of programming code, but must also include other supporting components: Unit Tests : Any implementation or modification of code should be follow by a relevant unit test. This ensures that any bugs that might be present are detected early, and can be easily traced and fixed. Change Control : It is not unusual to require changes to the design during the implementation stage. This might be due to a change in requirements, or an oversight during the design phase. These changes should be reflected in the design documents as well as the associated tests. Quality Assurance : All written code should be put through some form of Quality Assurance (QA) process. This might include code conformance checking, static code analysis, build tests, memory leak detection, or even peer review. The execution of tests and QA tools can be automated using build management tools listed in section 4.6. Using build tools, a list of predefined actions can be executed whenever changes to the code are detected, and notification sent out whenever a problem is found. 3.2 Test Design This section briefly discusses two classes of strategies commonly used when designing software test cases. These strategies provide a systematic approach towards creating test cases with higher chances of discovering errors in a program and are oriented around achieving sufficient test coverage – the black-box approach is designed to attain good input-output data coverage, while the white-box approach focuses instead on program logic coverage. 3.2.1 The Black Box approach The Black Box method, sometimes referred to as Data-Driven or Functional testing, involves taking an external perspective of the program units and ignoring the internal workings. Test cases are defined by setting up a range of different inputs, and comparing the results of each run against a predefined list of expected output. It is important that the expected output are predefined beforehand so as to avoid erroneous (but seemingly plausible) results from being interpreted at first glance as being acceptable. Initial test cases are often derived from the software specification, with each of the specified require- ments translated to a set of expected input-output. While this method is useful in exposing unimple- mented parts of the specification, it do not yet provide sufficient coverage of input data. 5
  • 9. To weed out all errors, test cases would have to include combinations of not just expected and valid inputs but all possible input. For example, in the case of a program reading from file a value represent- ing ‘age’, a valid input may be a range of positive integers, but possible inputs would also include 0, 0.12XY ZA123, 0.2e12, −12637213232, empty strings, character strings, binary data, etc. For most programs, this form of exhaustive input testing is not possible as it would involve an almost infinite number of test cases. Instead, a compromise is made by choosing a subset of test cases that has the highest probability of detecting the most errors. The following are several methodologies used to select an effective subset of test cases (Examples and detailed discussion on these methodologies are available in [2]). Equivalence Partitioning : The input domain is partitioned into a finite number of equivalence classes such that reasonable assumptions can be made that a test of a representative value of each class is equivalent to that of any other value in its class. Test cases can then be derived by gathering one value from each partition. Boundary-value analysis : This method complements equivalence partitioning by concentrating on elements at the borders of each class. Cause-effect graphing : Test cases are converted from rules in a decision table. This decision table is generated from a cause- effect graph which is a logical representation of the functionality that the program is attempting to attain. Error guessing : Test cases are written based on a list of error-prone conditions. Generation of the list relies on an understanding of the program implementation as well as the science represented by the program, and thus depends largely on the knowledge, creativity and experience of the developer. 3.2.2 The White Box approach The White Box approach, also known as Logic-Driven or Structural testing, uses an internal perspective of the program units where test cases are designed from an examination of the program logic. In its simplest form, the White Box method can be seen as an iterative design strategy driven by code coverage analysis2 . Using the White Box method, a testing solution can be designed along the lines of: 1. Decide on a reasonable coverage target. 2. Analyse coverage of current test cases. Initial test cases would ideally have been derived using the Black Box approach. 3. If total coverage is below the predetermined target, study low coverage segments of the code 4. Identify and include test cases that can increase coverage 5. Re-execute tests, and repeat steps 2-4 until target coverage is achieved A high coverage level of a test run would indicate that most of the logical paths within the program have been traversed, which leads to a higher chance of exposing errors within the program. Coverage level can therefore be used, to a certain extent, as a measure of the quality of the test cases. The following is a non-exhaustive list of different measures of coverage, each of which is increasingly more complete but harder to achieve. (Examples and detailed discussion on these methodologies are available in [2]). Statement Coverage : Ensuring that every statement is executed at least once. Decision Coverage : Ensuring that every branch direction is traversed, and (for subroutines with multiple entry points) that every entry point is invoked at least once. 2 Code coverage is a metric which describes the degree to which a program has been exercised, and can be determined using dynamic analysis tools. Some of these tools are presented in section 4.4 6
  • 10. Condition Coverage : Ensuring that each condition in a branch takes on all possible outcomes at least once, and that every entry point is invoked at least once. Decision-condition Coverage : Ensuring that both decision coverage and condition coverage conditions are satisfied. Multiple-condition Coverage : Ensuring that all possible combinations of condition outcomes in each decision are invoked at least once. White Box testing is very useful in ensuring that the test cases cover sufficient ground. It increases the probability of exposing errors, and prevents the problem of ‘pseudo success’ – a situation where developers are misled by a 100% passing rate when only a small portion of the program is tested. Since developers would have to analyse and understand implementation code in order to derive test cases, the White Box method comes with an added bonus of encouraging code inspection. Many errors and issues (such as unnecessary or over-complicated code) could be identified during this stage even before the tests are actually run. As useful as it might be, White Box testing does have some limitations. It cannot show that the program being tested meets its specifications; neither can it identify missing paths or data-dependent problems. Therefore, it should be noted this method would not by itself produce a complete solution, but should be used in conjunction with Black Box testing. 3.3 Deciding on a strategy The methodologies discussed above are generic, and can be applied to any of the testing stages defined in section 3.1.2. It is up to the developer to determine an overall strategy based on a combination of methodologies tailored to suit specific circumstances. A good strategy would be one that can strike a balance between the thoroughness of the tests and the resources invested in developing and executing the tests. An example of a test design strategy that might be drawn up by a developer would be: 1. Generate test cases based on all specification documents relevant to the code being tested. If the specification is in the form of input-output conditions, start with the cause-effect graphing technique. 2. Supplement that with boundary-value analysis and equivalence partitioning. Ensure that both valid and invalid conditions are covered. 3. Generate additional test cases based on error-guessing. 4. Add further test cases (based on analysing the code) until an 90% statement coverage is achieved3 . 3 An 85 − 95% statement coverage is usually a good initial target. However, one would ideally aim for full statement coverage and a sufficient level of coverage for Decision and/or Condition 7
  • 11. 4 Available Tools This chapter presents a survey of software testing tools currently available for software written in Fortran. We have broken down the list into several categories: • Testing Framework • Capture and Playback • Output Validation • Test Coverage • Test Management and Automation • Build Management The description for each tool were adapted from text available in their respective websites. 4.1 Testing Framework Testing frameworks accelerate the testing process by providing developers with tools that assist in the development and deployment of tests. While each of the frameworks employ different approaches, all of them should provide the following: • Tools and libraries for writing test suites (a collection of test cases), • Mechanism for setting up and tearing down a testing runtime environment, • Standardised form or reporting and managing test results. 4.1.1 pFUnit Available from http://sourceforge.net/projects/pfunit/ More info at http://opensource.gsfc.nasa.gov/projects/funit/pfunit.php License NASA Open Source Agreement (NOSA) Description The goals of the pFUnit project are to provide a shared mechanism for supporting unit testing within the HPC community in the hope of encouraging best practices for development and maintenance of software. In particular, pFUnit aims to be sufficiently minimal to encourage rapid adoption while still providing a minimum threshold of functionality. By providing pFUnit as open source, we hope to leverage interest from other groups to enhance portability and usability. pFUnit is a Fortran analogue to various other xUnit testing frameworks which have been developed within the software community, and is intended to enable test driven development (TDD) within the scientific/technical programming community. It was written (almost) entirely in standard conforming Fortran 95, and was developed using TDD methodology. pFUnit is bundled with an extensive set of self-tests which are intended to evolve along with the primary package. pFUnit includes scripts which can conveniently wrap user-written tests into test suites and assemble those suites into an executable. The lack of true object-orientation and reflection within Fortran neces- sitates this approach. Nonetheless, once added to a developer build system adding/running additional tests requires minimal effort. The executable itself is, at least for the moment, command-line driven. If all tests pass, then a simple summary of the number of tests run is returned. If some tests failed, a summary of which tests failed and any associated messages is returned to standard output. Features that will be of particular interest to the developer of scientific applications include: • Extensive sets of assert routines for floating point, including support for single and double precision, multidimensional arrays, and various means of expressing tolerances. 8
  • 12. • Ability to launch MPI tests and report results back as a single test - an essential feature for high-end computing associated with weather and climate modelling. • Ability to repeat tests across a complex high-dimensional parameter space. The need for this capability arises when multiple input parameters strongly interact within a subsystem. The ability to balance performance concerns against the need to adequately sample the possibilities is very useful. Failing parameter tests report back which combinations of parameters resulted in failures. 4.1.2 fUnit Available from http://funit.rubyforge.org/ License NASA Open Source Agreement (NOSA) Description FUnit is a unit testing framework for Fortran modules. Unit tests are written in Fortran fragments that use a small set of testing-specific keywords and functions. FUnit transforms these fragments into valid Fortran code and compiles, links, and runs them against the module under test. FUnit is opinionated software which values convention over configuration. Specifically, fUnit requires a Fortran 95 compiler, it only supports testing routines contained in modules, it requires tests to be stored along side the code under test, and it requires that you follow a specific naming rule for test files. The requirements for using fUnit are : • A Fortran 90/95/2003 compiler (set via FC environment variable) • The Ruby language with the RubyGems package manager 4.1.3 DejaGNU Available from http://www.gnu.org/software/dejagnu/ License GNU General Public License (GPL) Description DejaGnu is a framework for testing other programs. Its purpose is to provide a single front end for all tests. Think of it as a custom library of Tcl procedures crafted to support writing a test harness. A Test Harness is the testing infrastructure that is created to support a specific program or tool. Each program can have multiple testsuites, all supported by a single test harness. DejaGnu is written in Expect, which in turn uses Tcl (Tool command language). DejaGnu offers several advantages for testing: • The flexibility and consistency of the DejaGnu framework make it easy to write tests for any program. • DejaGnu provides a layer of abstraction which allows you to write tests that are portable to any host or target where a program must be tested. For instance, a test for GDB can run (from any Unix based host) on any target architecture that DejaGnu supports. Currently DejaGnu runs tests on several single board computers, whose operating software ranges from just a boot monitor to a full-fledged, Unix-like realtime OS. • All tests have the same output format. This makes it easy to integrate testing into other software development processes. • Using Tcl and Expect, it’s easy to create wrappers for existing testsuites. By incorporating existing tests under DejaGnu, it’s easier to have a single set of report analyse programs.. 9
  • 13. DejaGnu is written in Expect, which in turn uses Tcl (Tool command language). Running tests requires two things: the testing framework and the testsuites themselves. Tests are usually written in Expect using Tcl, but you can also use a Tcl script to run a testsuite that is not based on Expect. 4.1.4 QMTest Available from http://www.codesourcery.com/qmtest/ License GNU General Public License (GPL) Description QMTest is a cost-effective general purpose testing solution that can be used to implement a robust, easy- to-use testing process. QMTest runs on Windows and on most UNIX-like operating systems including GNU/Linux. QMTest’s extensible architecture allows it to handle a wide range of application domains: everything from compilers to graphical user interfaces to web-based applications. QMTest can easily compare test results to known-good baselines, making analysing test results far simpler. And, because QMTest runs on virtually all operating systems, you can use it with your entire product line. 4.1.5 Cleanscape Grayboxx Vendor Cleanscape Software International http://www.cleanscape.net/products/grayboxx/index.html Description A complete software life-cycle testing toolset developed for software written in C, Fortran, Ada, and Assembly. Grayboxx provides a complete software testing solution that verifies functional and structural performance requirements for mission critical applications. Grayboxx automatically conducts the follow- ing test methodologies: Blackbox Testing, Whitebox Testing, Regression Testing, Assertion Testing, and Mutation Testing. Grayboxx speeds the development process by allowing developers and test engineers to automatically: • Generate test cases • Conduct coverage analysis with complexity metrics • Conduct unit performance testing with no probe insertions • Generate test stubs • Generate test harnesses • Execute tests • Prepare modules • Verify results Grayboxx also allows for both full and partial regression testing, allowing the tester to run the same test more than once or to name the test titles to run with a subset of test cases. 10
  • 14. 4.2 Capture and Playback Capture and Playback tools provide an alternative mechanism for testing applications. Instead of having developers write test cases using scripts or code, these tools enable test cases to be created through the recording of input during a program execution. These input can then be played back during the test run, and the output compared to an expected input-output value. These tools are independent of the programming language used in the program, and are very useful for testing interactive programs (command-line interfaces or graphical user interfaces). These tools may be a quick solution to preparing functional or acceptance tests. 4.2.1 AutoExpect Available from : http://expect.nist.gov/ License None (public domain) Description Expect is a tool for automating interactive programs. It is possible to make very sophisticated Expect scripts. For example, different patterns can be expected simultaneously either from one or many processes, with different actions in each case. Traditional control structures such as if/then/ else, procedures, and recursion are available. Expect’s language facilities are provided by Tcl, a very traditional scripting language. Traditionally, users write Expect scripts by studying the interaction to be automated and writing the corresponding Expect commands to perform the interaction. Using Autoexpect, this stage could be automated. Autoexpect, which is part of the Expect distribution, is a program which watches a user interacting with another program and creates an Expect script that reproduces the interactions. For testing an interactive application, the Expect script generated by AutoExpect can be modified and repeatedly played back with different input values. 4.2.2 TestWorks CAPBAK Vendor Software Research, Inc. (http://www.soft.com/Products/stwindex.html) Description CAPBAK is a capture/playback tool system which allows the user to record mouse movements, keyboard activities, widget calls and verification information into a test script language for later use. CAPBAK supports automatic synchronisation feature that handle minor application changes and time-sensitive operations. Captured images and/or character patterns provide baselines against which future runs of the tests are compared. CAPBAK/X’s automatic output synchronisation ensures reliable playback, allowing tests to be run unsupervised as often as required. With the Xvirtual display capability, test runs can be executed in the background, freeing the screen for other activities. This capability can also be used to invoke a single application multiple times on the same workstation, thus allowing for load testing in a client/server environment. Used in conjunction with its TestWorks/Regression companion tools, EXDIFF and SMARTS, the regression testing process can be completely automatic. The SMARTS test management system organises CAPBAK/X’s test sessions into a hierarchical structure for execution individually or as a part of a test suite. This process is based on the verification criteria selected for each test. Discrepancies are reported by SMARTS for further analysis. Extraneous discrepancies can be masked during the comparison process via EXDIFF. Following test execution, SMARTS logs the test statistics and generates PASS/FAIL results into various standard reports. 4.3 Output validation Output validation tools provide a convenient mechanism to compare results from a test run to that of a gold standard or acceptable value. They automate the tedious task of performing result validation 11
  • 15. especially for programs with lots of output, or for multiple runs with different values. Tools such as ndiff and numdiff are particularly useful for scientific software as they can be config- ured with acceptable error tolerances when performing comparisons of numerical data. This avoids tiny deviations in floating point data from being unnecessarily flagged as errors. 4.3.1 TextTest Available from: http://sourceforge.net/projects/texttest Project website: http://texttest.carmen.se/ License OSI-Approved Open Source Description TextTest works via comparing plain text logged by programs with a previous ’gold standard’ version of that text. The focus is around testing a particular executable program with a variety of inputs. To start with, a plain text configuration file is created that tells TextTest about your program, how to run it, and how to test it. Tests (and test suites) are then defined entirely using plain text files in a directory structure. A test is defined partly by the expected files and their contents that should be produced, and partly by the ’input’ to provide, which can consist any or all of: • Options to be provided on the command line • A file to be redirected to standard input • Environment variables that should be set • A sequence of ’use-case’ actions to be performed on a GUI Any output at all can be compared, so long as it is plain text, or can be converted to it. 4.3.2 ndiff Available from: http://www.math.utah.edu/~beebe/software/ndiff/ License GNU General Public License (GPL) Description Suppose that you have just run the same numerical program in two different environments, perhaps different compilers on the same system, or on different CPU architectures or operating systems. You run diff on the two program output text files, but there are thousands of differences reported. Is the program behaving acceptably, or are there real errors that you must deal with, perhaps due to architectural assumptions in your code, or to numerical instabilities in your algorithms, or errors in compiler-generated code, or errors or inaccuracies in run-time libraries? ndiff is a very useful tool for solving problems like that. Simply put, it assumes that you have two text files containing numerical values, and the two files are expected to be identical, or at least numerically similar. ndiff allows you to specify absolute and/or relative error tolerances for differences between numerical values in the two files, and then reports only the lines with values exceeding those tolerances. It also tells you by how much they differ. 4.3.3 Toldiff Available from: http://sourceforge.net/projects/toldiff/ License MIT License 12
  • 16. Description Toldiff is a diff tool that allows tolerable (insignificant) differences between two files to be suppressed showing only the important ones. The tolerable differences are recorded running the tool with an appro- priate command line flag. 4.3.4 numdiff Available from: http://www.nongnu.org/numdiff/ License GNU General Public License (GPL) Description Numdiff is a little program that can be used to compare putatively similar files line by line and field by field, ignoring small numeric differences or/and different numeric formats. Equivalently, Numdiff is a program with the capability to appropriately compare files containing numerical fields (and not only). By default, Numdiff assumes the fields are separated by white spaces (blanks, horizontal tabulations and newlines), but the user can also specify its list of separators. When you compare a couple of such files, what you want to obtain usually is a list of the numerical fields in the second file which numerically differ from the corresponding fields in the first file. Well known tools like diff, cmp or wdiff can not be used to this purpose: they can not recognise whether a difference between two numerical fields is only due to the notation or is actually a difference of numerical values. Moreover, you could also want to ignore differences in numerical values as long as they do not overcome a certain threshold. In other words, you could desire to neglect all small numerical differences too. 4.4 Test Coverage Code coverage of test suites can be determined using dynamic analysis tools, some of which are listed in this section. This information is useful when determining the thoroughness of the test cases, and are often used when designing test cases using the White Box method (see section 3.2.2). Using code coverage tools, developers can determine: Cold spots Parts of the code that are never used, or just not used by the test cases. Hot spots Parts of the code that are used frequently. New test cases To exercise a part of the code not already tested. This kind of testing is really a test of the completeness of the test cases, i.e. do they exercise all parts of the code but it also gives indirect testing of the code itself. If all cases had the same cold spot(s) then maybe that code can be removed, or if there is a common hot spot then this is an area to study in detail to find ways of making it more efficient. 4.4.1 gcov Available from: http://sourceforge.net/project/showfiles.php?group_id=3382 License GNU General Public License (GPL) Description gcov is a test coverage program. Use it in concert with GNU CC to analyse your programs to help create more efficient, faster running code. You can use gcov as a profiling tool to help discover where your optimisation efforts will best affect your code. You can also use gcov along with the other profiling tool, gprof, to assess which parts of your code use the greatest amount of computing time. Profiling tools help you analyse your code’s performance. Using a profiler such as gcov or gprof, you can find out some basic performance statistics, such as: • how often each line of code executes 13
  • 17. • what lines of code are actually executed • how much computing time each section of code uses Once you know these things about how your code works when compiled, you can look at each module to see which modules should be optimized. gcov helps you determine where to work on optimization. Software developers also use coverage testing in concert with testsuites, to make sure software is actually good enough for a release. Testsuites can verify that a program works as expected; a coverage program tests to see how much of the program is exercised by the testsuite. Developers can then determine what kinds of test cases need to be added to the testsuites to create both better testing and a better final product. Output from gcov can be visualised using tools such as ggcov (http://ggcov.sourceforge.net/) and lcov (http://ltp.sourceforge.net/coverage/lcov.php). 4.4.2 Polyhedron plusFort - CVRANAL Vendor Polyhedron Software http://www.polyhedron.co.uk/pf/pfqa.html#coverage Description The plusFort package includes CVRANAL, a coverage analysis facility that places probes into Fortran source code which allow users to monitor the effectiveness of testing. At the end of each run, the probes update the coverage statistics for each source file. This data may be analysed at any time using the CVRANAL tool. CVRANAL identifies untested code blocks, and execution hot-spots. In addition, CVRANAL can annotate your source code as shown below. The annotations are com- ments and do not affect the validity of the source code. 4.4.3 FCAT Available from http://www.dl.ac.uk/TCSC/UKHEC/FCAT/ Description FACT is similar to CRVANAL in that it reports the execution count for each line of executable source code. FCAT (FORTRAN Coverage Analysis Tool) is used for the Coverage Analysis of FORTRAN codes. • finding out ”cold-spot” in Fortran codes (the part of the codes that are never executed), and flags these parts line-by-line. • finding out ”hot-spot” in Fortran codes (the part of the codes that are most frequently executed), and gives a line by line profile. It is designed to working mainly with F90/F95, even through it also works with fixed formatted FORTRAN, thus F77. FCAT offers some facility for the coverage analysis of parallel codes. It treats a line as being executed if at least one processor has executed it. The counter for the line is taken as the maximum of the number of times this line has been executed over all processors. 4.4.4 Cleanscape Grayboxx Vendor Cleanscape Software International http://www.cleanscape.net/products/grayboxx/index.html Description A complete software life-cycle testing toolset that includes coverage analysis with complexity metrics. It can perform the following coverage functions 14
  • 18. • Measure test effectiveness and reliability of testing by analysing application source code • Set up test cases and measures their efficiency • Consolidate results of test coverage measurements for several scenarios or during a test campaign • Enable effective visualisation of covered and uncovered source code 4.4.5 TestWorks/TCAT Vendor Software Research, Inc. http://www.soft.com/Products/stwindex.html Description TCAT and S-TCAT, a branch-level unit-test and system test coverage analysis tool, provides branch and call-pair coverage for F77 and Ada programs. TCAT and S-TCAT measure the number of times each segment or function-call pair is exercised. C1 expresses test effectiveness as the percentage of every segment exercised in a program by a test suite, relative to the number of such segments existing in the system. S1 expresses test effectiveness as the percentage of every function-call exercised in a program by a test suite, relative to the number of such function-calls existing in the system. TCAT and S-TCAT instrument the application by placing markers at each segment or function-call. When test cases have been run against the instrumented code, TCAT/S-TCAT collects and stores test coverage data in a tracefile. TCAT/S-TCAT then extracts this information to create coverage reports indicating which calls remain untested or frequently tested, and which test cases duplicate coverage. TCAT/S-TCAT also creates archive files containing cumulative test information. The instrumentation process also generates call-trees that identify a program’s modules and represent the caller-callee hierarchical structure (as well as subtrees of caller-callee dependencies) within a program. Using optional user annotation and or supplied colour annotation, the call-tree shows each functions’ level of interface exercise. When a function-call coverage values are low the user can navigate directly to the corresponding source code. The call-trees can also generate directed graphs depicting the control-flow structure for individual modules. 4.4.6 LDRA Testbed Vendor LDRA http://www.ldra.co.uk/testbed.asp Description LDRA Testbed’s Dynamic Analysis tool provides coverage analysis at the following levels: • Statement Coverage • Branch/Decision Coverage • LCSAJ Coverage • MC/DC Coverage • Dynamic Data Flow Coverage 4.4.7 McCabe IQ Vendor McCabe Software http://www.mccabe.com/iq_test.htm 15
  • 19. Description McCabe IQ provides comprehensive test / code coverage to focus, monitor, and document software testing processes. Using industry-standard testing methods and advanced dynamic analysis techniques, McCabe IQ accurately assesses the thoroughness of your testing and aids in gauging the time and resources needed to ensure a well-tested application. McCabe IQ provides multiple levels of test coverage at the unit, integration, regression test phases including module, lines of code, branch, path, Boolean (MC/DC for DO-178B test verification), data, class (OO), and architectural coverages. 4.5 Test Management and Automation As the number of test cases for each project grows, it becomes increasingly important for these tests to be organised and automated as much as possible. Most test management and automation tools would provide the following functionalities: • Organisation of information such as software requirements, test plans, and test cases. • Test results tracking. • Automated execution of tests. Test runs can be periodic, or triggered by events such as changes to the source tree. • Reports and statistics generation. 4.5.1 RTH Available from: http://www.rth-is-quality.com License GNU General Public License (GPL) Description rth is a web-based tool designed to manage requirements, tests, test results, and defects throughout the application life cycle. The tool provides a structured approach to software testing and increases the vis- ibility of the testing process by creating a common repository for all test assets including requirements, test cases, test plans, and test results. Regardless of their geographic location, rth allows testers, devel- opers, business analysts, and managers to monitor and gauge application readiness. The tool includes modules for requirements management, test planning, test execution, defect tracking, and reporting. Benefits of RTH include: • Working in remote locations is no longer a problem. View the status of your project on the web • View progress of requirements, test execution, and bug status in real-time • All documents (requirements, tests, test plans, supporting documents) are stored under version control • Store record or file-based requirements based on your reporting needs • Test Tool agnostic! Take advantage of test automation with three simple functions that allow you to write automated test results to rth • Post and report on both manual and automated test results 4.5.2 TestLink Available from: http://testlink.org License GNU General Public License (GPL) 16
  • 20. Description TestLink is a open source web based Test Management and test Execution system. which allow quality assurance teams to create and manage their test cases as well as organise them into test plans. These test plans allow team members to execute test cases and track test results dynamically, generate reports, trace software requirements, prioritise and assign. The tool is based on PHP, MySQL, and includes several other open source tools. It also supports Bug tracking systems as is Bugzilla or Mantis. In short, TestLink allow users to: • Collect and organise test cases dynamically • Track results and metrics associated with test execution • Track specific information about individual tests • Capture and report details to assist in conducting a more thorough testing process • Customise TestLink to fit requirements and processes 4.5.3 QaTraq Available from: http://www.testmanagement.com License GNU General Public License (GPL) with options for commercial upgrades. Professional upgrades include additional modules for extended graphical reporting capabilities and extensible scripting functionalities. Vendor Traq Software Ltd. Description QaTraq Test Case Management Tool allows you to consolidate the manual and functional software testing process. With one functional software testing management tool we give you the control to automate your own techniques and strategies to track your testing, from the planning stages right through to the test completion reporting. From communicating your test plans to managing the functional coverage of your software testing QaTraq can help you gain control of the whole manual and functional software testing process without changing your own strategies or techniques. Amongst other things QAtraq can provide you with: • Improved co-ordination between testers, team leaders and managers • A repository of your entire manual testing progress • A knowledge base of technical testing to share amongst a test team • A formal channel for developers and testers to suggest tests • Accurate tracking of your functional software testing • Instant reports based on test cases created and executed • Statistics listing the testing which is most effective • Control of your Manual and functional software testing. 4.5.4 AutoTest Available from http://eiffelzone.com/esd/tstudio/ 17
  • 21. License Eiffel Forum License, version 2 http://www.opensource.org/licenses/ver2_eiffel.php Description AutoTest (formerly TestStudio) is a fully automatic testing tool based on Design by Contract. Contracts are a valuable source of information regarding the intended semantics of the software. The information that contracts (preconditions, postconditions, class invariants, loop variants and invariants, and check instructions) provide can be used to check whether the software fulfils its intended purpose. By checking that the software respects its contracts, we can ascertain its validity. Therefore, contracts provide the basis for automation of the testing process. AutoTest allows the user to generate, compile and run tests at the push of a button. 4.5.5 STAF Available from: http://staf.sourceforge.net License Common Public License (CPL) V1.0. http://www.opensource.org/licenses/cpl1.0.php Description The Software Testing Automation Framework (STAF) is an open source, multi-platform, multi-language framework designed around the idea of reusable components, called services (such as process invocation, resource management, logging, and monitoring). STAF removes the tedium of building an automation infrastructure, thus enabling you to focus on building your automation solution. The STAF framework provides the foundation upon which to build higher level solutions, and provides a pluggable approach supported across a large variety of platforms and languages. STAF can be leveraged to help solve common industry problems, such as more frequent product cycles, less preparation time, reduced testing time, more platform choices, more programming language choices, and increased National Language requirements. STAF can help in these areas since it is a proven and mature technology, promotes automation and reuse, has broad platform and language support, and provides a common infrastructure across teams. STAX is an execution engine which can help you thoroughly automate the distribution, execution, and results analysis of your testcases. STAX builds on top of three existing technologies, STAF, XML, and Python, to place great automation power in the hands of testers. STAX also provides a powerful GUI monitoring application which allows you to interact with and monitor the progress of your jobs. Some of the main features of STAX are: support for parallel execution, user-defined granularity of execution control, support for nested testcases, the ability to control the length of execution time, the ability to import modules at run-time, support for existing Python and Java modules and packages, and the ability to extend both the STAX language as well as the GUI monitoring application. Using these capabilities, you can build sophisticated scripts to automate your entire test environment, while ensuring maximum efficiency and control. Other STAF services are also provided to help you to create an end-to-end automation solution. By using these services in your test cases and automated solutions, you can develop more robust, dynamic test cases and test environments. 4.6 Build Management Build management systems automate the update-compile-test cycle of a software project. Changes to the source code would trigger a rebuild of the application and cause the tests to be executed. This allows developers to get immediate feedback if an error occurs, and ensures that problems are detected as early as possible. Other tasks, such as software quality assurance analysis, can be included within the list of actions to be performed. This ensures that the updated source code is not only correct, but is also of good quality and adheres to standards defined for the project. 18
  • 22. 4.6.1 BuildBot Available from: http://buildbot.sourceforge.net License GNU General Public License (GPL) Description The BuildBot is a system to automate the compile/test cycle required by most software projects to validate code changes. By automatically rebuilding and testing the tree each time something has changed, build problems are pinpointed quickly, before other developers are inconvenienced by the failure. The guilty developer can be identified and harassed without human intervention. By running the builds on a variety of platforms, developers who do not have the facilities to test their changes everywhere before checkin will at least know shortly afterwards whether they have broken the build or not. Warning counts, lint checks, image size, compile time, and other build parameters can be tracked over time, are more visible, and are therefore easier to improve. The overall goal is to reduce tree breakage and provide a platform to run tests or code-quality checks that are too annoying or pedantic for any human to waste their time with. Developers get immediate (and potentially public) feedback about their changes, encouraging them to be more careful about testing before checkin. 4.6.2 test-AutoBuild Available from: http://www.autobuild.org/ License GNU General Public License (GPL) Description Test-AutoBuild is a framework for performing continuous, unattended, automated software builds. The idea of Test-AutoBuild is to automate the building of a project’s complete software stack on a pristine system from the high level applications, through the libraries and right down to the smallest part of the toolchain. 4.6.3 Parabuild Vendor Viewtier Systems http://www.viewtier.com/products/parabuild/ Description Parabuild is a software build management server that helps software teams and organisations reduce risks of project failures by providing practically unbreakable daily builds and continuous integration builds. Parabuild features an effortless installation process and easy overall use, multi-platform remote builds, fast Web user interface, a wide set of supported version control, and issue tracking systems. 4.6.4 CruiseControl Available from: http://cruisecontrol.sourceforge.net License BSD-style License http://www.opensource.org/licenses/bsd-license.php 19
  • 23. Description CruiseControl is composed of 2 main modules: • the build loop: core of the system, it triggers build cycles then notifies various listeners (users) using various publishing techniques. The trigger can be internal (scheduled or upon changes in a SCM) or external. It is configured in a xml file which maps the build cycles to certain tasks, thanks to a system of plugins. Depending on configuration, it may produce build artefacts. • the reporting allows the users to browse the results of the builds and access the artefacts 4.6.5 BuildForge Vendor Recently acquired by IBM and incorporated into IBM’s Rational Software suite. http://www.buildforge.com Description IBM Rational Build Forge provides complete build and release process management through an open framework that helps development teams standardise and automate tasks and share information. Our products can help clients accelerate software delivery, improve software quality, as well as meet audit and compliance mandates. 4.6.6 AEGIS Available from: http://aegis.sourceforge.net/ License GNU General Public License (GPL) Description Aegis is a transaction-based software configuration management system. It provides a framework within which a team of developers may work on many changes to a program independently, and Aegis co- ordinates integrating these changes back into the master source of the program, with as little disruption as possible. While Aegis is not a build management system, we included it within this section as it can be used to the same effect of ensuring that code passes tests (build, testing, QA) before being merged into main source tree. 5 Further reading 1. B. Kleb & B. Wood, Computational Simulations and the Scientific Method, NASA Langley Research Center, (2005). 2. A.H. Watson & T.J. McCabe, Structured Testing: A Testing Methodology Using the Cyclomatic Complexity Metric, National Institute of Standards and Technology, (1996). 3. G. Dodig-Crnkovic, Scientific Methods in Computer Science, Department of Computer Science, Mlardalen University, (2002) 4. E. Dustin, Effective Software Testing: 50 Specific ways to improve your testing, Addison Wesley Professionals, (2002). 5. B. Beizer, Software Testing Techniques, Van Nostrand Reinhold Co., (1990). 6. M. Fewster and D. Graham, Software Test Automation: Effective use of test execution tools, Addison-Wesley, (1999). 7. S.M. Baxter, S.W. Day, J.S. Fetrow & S.J. Reisinger, Scientific Software Development Is Not an Oxymoron, PLoS Comput Biol 2(9): e87, (2006). 20
  • 24. 8. D. Libes, How to Avoid Learning Expect or Automating Automating Interactive Programs, Proceed- ings of the Tenth USENIX System Administration Conference (LISA X), (1996). 9. S. Cornett, Code Coverage Analysis, Bullseye Testing Technology. http://www.bullseye.com/ coverage.html 10. W.R. Bush, J.D. Pincus & D.J. Sielaff, A Static Analyzer for Finding Dynamic Programming Errors, Intrinsa Corporation, (2000). References [1] D.J. Worth & C. Greenough, A Survey of Software Tools for Computational Science, Technical Report RAL-TR-2006-011, CCLRC Rutherford Appleton Laboratory (2006). [2] G.J. Myers, The Art of Software Testing, 2nd Edition, John Wiley & Sons inc., (2004). 21