Selecting Software Test Data Using Data Flow Information
IEEE Transactions on Software Engineering
A methodology for controlling the size of a test suite
ACM Transactions on Software Engineering and Methodology (TOSEM)
Empirical Studies of a Safe Regression Test Selection Technique
IEEE Transactions on Software Engineering
Prioritizing test cases for regression testing
Proceedings of the 2000 ACM SIGSOFT international symposium on Software testing and analysis
Qualitative Interpretation of Sensor Patterns
IEEE Expert: Intelligent Systems and Their Applications
An empirical evaluation of system and regression testing
CASCON '02 Proceedings of the 2002 conference of the Centre for Advanced Studies on Collaborative research
Using Component Metacontent to Support the Regression Testing of Component-Based Software
ICSM '01 Proceedings of the IEEE International Conference on Software Maintenance (ICSM'01)
A Study of Effective Regression Testing in Practice
ISSRE '97 Proceedings of the Eighth International Symposium on Software Reliability Engineering
Observations and problems applying ART2 for dynamic sensor pattern interpretation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Degree of familiarity ART2 in knowledge-based landmine detection
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Modified ART 2A growing network capable of generating a fixed number of nodes
IEEE Transactions on Neural Networks
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Regression testing applies a previously developed test case suite to new software versions. A traditional approach is the execution of all test cases, although, this may be time consuming and, sometimes, not necessary as the source code modification may affect only a test case subset. Some initiatives have addressed this issue. For instance, one of the most promising ones is the modified-based technique that selects test cases based on whether they execute the modified parts of the program. This technique is conservative, but it often selects test cases that are not relevant. This article presents an approach to select test case subsets by using an Adaptive Resonance Theory-2A self-organising neural network architecture. In this approach, test cases are summarised in feature vectors with code coverage information, which are classified by the neural network in clusters. Clusters are labelled representing each software functionality evaluated by the coverage criterion. A new software version is then analysed to determine modified points and, then, clusters, which represent the related functionalities, are chosen. The test case subset is obtained from these clusters. Experiments were conducted to evaluate the approach using feature vectors based on all-uses and -nodes code coverage information against a modification-based technique.