Mapping the effectiveness of automated test suite generation techniques

Carlos Oliveira, Aldeida Aleti, Lars Grunske, Kate Smith-Miles

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)

Abstract

Automated test suite generation (ATSG) is an important topic in software engineering, with a wide range of techniques and tools being used in academia and industry. While their usefulness is widely recognized, due to the labor-intensive nature of the task, the effectiveness of the different techniques in automatically generating test cases for different software systems is not thoroughly understood. Despite many studies introducing various ATSG techniques, much remains to be learned, however, about what makes a particular technique work well (or not) for a specific software system. In this paper, we seek an answer to the question: 'What features of a software system impact the effectiveness of ATSG techniques?' Once these features are identified, can they be used to select the most effective ATSG technique for a particular software system? To this end, we have implemented the mapping the effectiveness of test automation (META) tool, a new framework that identifies important software features that can be used to select suitable ATSG techniques to apply to new software systems. We evaluate the framework on a large set of open-source software projects and three ATSG techniques. The evaluation indicates that the number of methods in a class, the coupling between object classes, and the response for a class are the most indicative of what makes a software system hard to test by different techniques. The decision tree for ATSG technique selection generated by the META framework has an 88% accuracy, as shown by $n$-fold cross validation.

Original languageEnglish
Article number8375677
Pages (from-to)771-785
Number of pages15
JournalIEEE Transactions on Reliability
Volume67
Issue number3
DOIs
Publication statusPublished - Sep 2018

Keywords

  • Search-based software testing

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