Enhancing traditional behavioral testing through program slicing

Although there has been much research on the application of program slicing to the problem of software testing, most of it has focussed on regression testing. The objective of the published techniques is to reduce its cost by identifying the set of existing test cases which are guaranteed to exercise the modified program components. In this research, program slicing is applied to behavioral testing. Three testing approaches are presented to ensure that modifications made to a program have not adversely affected its correct behavior. The proposed testing techniques, as well as the underlying dynamic program slicing algorithm, are implemented as part of the CONCEPT research project. A case study using the JUnit testing framework is also presented to demonstrate their applicability in detecting faults, which could escape from traditional testing techniques

Regression Test Selection by Exclusion

This thesis addresses the research in the area of regression testing. Software systems change and evolve over time. Each time a system is changed regression tests have to be run to validate these changes. An important issue in regression testing is how to minimise reuse the existing test cases of original program for modied program. One of the techniques to tackle this issue is called regression test selection technique. The aim of this research is to signicantly reduce the number of test cases that need to be run after changes have been made. Specically, this thesis focuses on developing a model for regression test selection using the decomposition slicing technique. Decomposition slicing provides a technique that is capable of identifying the unchanged parts of the system. The model of regression test selection based on decomposition slicing and exclusion of test cases was developed in this thesis. The model is called Regression Test Selection by Exclusion (ReTSE) and has four main phases. They are Program Analysis, Comparison, Exclusion and Optimisation phases. The validity of the ReTSE model is explored through the application of a number of case studies. The case studies tackle all types of modication such as change, delete and add statements. The case studies have covered a single and combination types of modication at a time. The application of the proposed model has shown that signicant reductions in the number of test cases can be achieved. The evaluation of the model based on an existing framework and comparison with another model also has shown promising results. The case studies have limited themselves to relatively small programs and the next step is to apply the model to larger systems with more complex changes to ascertain if it scales up. While some parts of the model have been automated tools will be required for the rest when carrying out the larger case studies

Reducing regression test size by exclusion.

Operational software is constantly evolving. Regression testing is used to identify the unintended consequences of evolutionary changes. As most changes affect only a small proportion of the system, the challenge is to ensure that the regression test set is both safe (all relevant tests are used) and unclusive (only relevant tests are used). Previous approaches to reducing test sets struggle to find safe and inclusive tests by looking only at the changed code. We use decomposition program slicing to safely reduce the size of regression test sets by identifying those parts of a system that could not have been affected by a change; this information will then direct the selection of regression tests by eliminating tests that are not relevant to the change. The technique properly accounts for additions and deletions of code. We extend and use Rothermel and Harrold’s framework for measuring the safety of regression test sets and introduce new safety and precision measures that do not require a priori knowledge of the exact number of modification-revealing tests. We then analytically evaluate and compare our techniques for producing reduced regression test sets

Reducing regression test size by exclusion.

Operational software is constantly evolving. Regression testing is used to identify the unintended consequences of evolutionary changes. As most changes affect only a small proportion of the system, the challenge is to ensure that the regression test set is both safe (all relevant tests are used) and unclusive (only relevant tests are used). Previous approaches to reducing test sets struggle to find safe and inclusive tests by looking only at the changed code. We use decomposition program slicing to safely reduce the size of regression test sets by identifying those parts of a system that could not have been affected by a change; this information will then direct the selection of regression tests by eliminating tests that are not relevant to the change. The technique properly accounts for additions and deletions of code. We extend and use Rothermel and Harrold’s framework for measuring the safety of regression test sets and introduce new safety and precision measures that do not require a priori knowledge of the exact number of modification-revealing tests. We then analytically evaluate and compare our techniques for producing reduced regression test sets

ConSUS: A light-weight program conditioner

Program conditioning consists of identifying and removing a set of statements which cannot be executed when a condition of interest holds at some point in a program. It has been applied to problems in maintenance, testing, re-use and re-engineering. All current approaches to program conditioning rely upon both symbolic execution and reasoning about symbolic predicates. The reasoning can be performed by a ‘heavy duty’ theorem prover but this may impose unrealistic performance constraints. This paper reports on a lightweight approach to theorem proving using the FermaT Simplify decision procedure. This is used as a component to ConSUS, a program conditioning system for the Wide Spectrum Language WSL. The paper describes the symbolic execution algorithm used by ConSUS, which prunes as it conditions. The paper also provides empirical evidence that conditioning produces a significant reduction in program size and, although exponential in the worst case, the conditioning system has low degree polynomial behaviour in many cases, thereby making it scalable to unit level applications of program conditioning

Search-based amorphous slicing

Amorphous slicing is an automated source code extraction technique with applications in many areas of software engineering, including comprehension, reuse, testing and reverse engineering. Algorithms for syntax-preserving slicing are well established, but amorphous slicing is harder because it requires arbitrary transformation; finding good general purpose amorphous slicing algorithms therefore remains as hard as general program transformation. In this paper we show how amorphous slices can be computed using search techniques. The paper presents results from a set of experiments designed to explore the application of genetic algorithms, hill climbing, random search and systematic search to a set of six subject programs. As a benchmark, the results are compared to those from an existing analytical algorithm for amorphous slicing, which was written specifically to perform well with the sorts of program under consideration. The results, while tentative at this stage, do give grounds for optimism. The search techniques proved able to reduce the size of the programs under consideration in all cases, sometimes equaling the performance of the specifically-tailored analytic algorithm. In one case, the search techniques performed better, highlighting a fault in the existing algorith