%0 Conference Proceedings
%T Inspecting Code Churns to Prioritize Test Cases
%+ University of Naples Federico II = Università degli studi di Napoli Federico II
%A Altiero, Francesco
%A Corazza, Anna
%A Di Martino, Sergio
%A Peron, Adriano
%A Starace, Luigi
%Z Part 4: Testing Methods and Automation
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 32th IFIP International Conference on Testing Software and Systems (ICTSS)
%C Naples, Italy
%Y Valentina Casola
%Y Alessandra De Benedictis
%Y Massimiliano Rak
%I Springer International Publishing
%3 Testing Software and Systems
%V LNCS-12543
%P 272-285
%8 2020-12-09
%D 2020
%R 10.1007/978-3-030-64881-7_17
%K Regression testing
%K Test prioritization
%K Code churn.
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X Within the context of software evolution, due to time-to-market pressure, it is not uncommon that a company has not enough time and/or resources to re-execute the whole test suite on the new software version, to check for non-regression. To face this issue, many Regression Test Prioritization techniques have been proposed, aimed at ranking test cases in a way that tests more likely to expose faults have higher priority. Some of these techniques exploit code churn metrics, i.e. some quantification of code changes between two subsequent versions of a software artifact, which have been proven to be effective indicators of defect-prone components. In this paper, we first present three new Regression Test Prioritization strategies, based on a novel code churn metric, that we empirically assessed on an open source software system. Results highlighted that the proposal is promising, but that it might be further improved by a more detailed analysis on the nature of the changes introduced between two subsequent code versions. To this aim, in this paper we also sketch a more refined approach we are currently investigating, that quantifies changes in a code base at a finer grained level. Intuitively, we seek to prioritize tests that stress more fault-prone changes (e.g., structural changes in the control flow), w.r.t. those that are less likely to introduce errors (e.g., the renaming of a variable). To do so, we propose the exploitation of the Abstract Syntax Tree (AST) representation of source code, and to quantify differences between ASTs by means of specifically designed Tree Kernel functions, a type of similarity measure for tree-based data structures, which have shown to be very effective in other domains, thanks to their customizability.
%G English
%Z TC 6
%Z WG 6.1
%2 https://inria.hal.science/hal-03239823/document
%2 https://inria.hal.science/hal-03239823/file/497758_1_En_17_Chapter.pdf
%L hal-03239823
%U https://inria.hal.science/hal-03239823
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC6
%~ IFIP-WG6-1
%~ IFIP-ICTSS
%~ IFIP-LNCS-12543