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Regression testing is a testing activity that is performed to provide confidence that changes do not harm the existing behaviour of the software. Test suites tend to grow in size as software evolves, often making it too costly to execute entire test suites. A number of different approaches have been studied to maximize the value of the accrued test suite: minimization, selection and prioritization. Test suite minimization seeks to eliminate redundant test cases in order to reduce the number of t

Previous work has treated test case selection as a single objective optimisation problem. This paper introduces the concept of Pareto efficiency to test case selection. The Pareto efficient approach takes multiple objectives such as code coverage, past fault-detection history and execution cost, and constructs a group of non-dominating, equivalently optimal test case subsets. The paper describes the potential bene?ts of Pareto efficient multi-objective test case selection, illustrating with empi

SUMMARY Regression testing is a testing activity that is performed to provide confidence that changes do not harm the existing behaviour of the software. Test suites tend to grow in size as software evolves, often making it too costly to execute entire test suites. A number of different approaches have been studied to maximize the value of the accrued test suite: minimization, selection and prioritization. Test suite minimization seeks to eliminate redundant test cases in order to reduce the num

Pair-wise comparison has been successfully utilised in order to prioritise test cases by exploiting the rich, valuable and unique knowledge of the tester. However, the prohibitively large cost of the pair-wise comparison method prevents it from being applied to large test suites. In this paper, we introduce a cluster-based test case prioritisation technique. By clustering test cases, based on their dynamic runtime behaviour, we can reduce the required number of pair-wise comparisons significantl

Regression testing is a testing activity that is performed to provide confidence that changes do not harm the existing behaviour of the software. Test suites tend to grow in size as software evolve, often making it too costly to execute entire test suites. A number of different approaches have been studied to maximise the value of the accrued test suite: minimisation, selection and prioritisation. Test suite minimisation seeks to eliminate redundant test cases in order to reduce the number of te

Test case prioritization techniques seek to maximize early fault detection. Fault localization seeks to use test cases already executed to help find the fault location. There is a natural interplay between the two techniques; once a fault is detected, we often switch focus to fault fixing, for which localization may be a first step. In this article we introduce the Fault Localization Prioritization (FLP) problem, which combines prioritization and localization. We evaluate three techniques: a nov

We report on the application of Genetic Programming to Software Fault Localisation, a problem in the area of Search-Based Software Engineering (SBSE). We give both empirical and theoretical evidence for the human competitiveness of the evolved fault localisation formulæ under the single fault scenario, compared to those generated by human ingenuity and reported in many papers, published over more than a decade. Though there have been previous human competitive results claimed for SBSE problems,

SUMMARY Existing automated test data generation techniques tend to start from scratch, implicitly assuming that no pre‐existing test data are available. However, this assumption may not always hold, and where it does not, there may be a missed opportunity; perhaps the pre‐existing test cases could be used to assist the automated generation of additional test cases. This paper introduces search‐based test data regeneration, a technique that can generate additional test data from existing test dat

Abstract Automated debugging techniques have the potential to reduce developer effort in debugging. However, while developers want rationales for the provided automatic debugging results, existing techniques are ill-suited to provide them, as their deduction process differs significantly froof human developers. Inspired by the way developers interact with code when debugging, we propose Automated Scientific Debugging ( AutoSD ), a technique that prompts large language models to automatically gen

Spectrum Based Fault Localisation (SBFL) techniques rely on risk assessment formulae to convert program execution spectrum into risk evaluation values, which are in turn used to rank program statements according to their relative suspiciousness with respect to the observed failure. Recent work proved equivalence and hierarchy between different formulae, identifying a few groups of maximal formulae, i.e., formulae that do not dominate each other. The holy grail in the field has been to come up wi

Companies such as Google tend to develop products from one continually evolving core of code. Software is neither shipped, nor released in the traditional sense. It is simply made available, with dramatically compressed release cycles regression testing. This large scale rapid release environment creates challenges for the application of regression test optimisation techniques. This paper reports initial results from a partnership between Google and the CREST centre at UCL aimed at transferring