Mapping hardness of Automated Software Testing

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Abstract

Automated Test Case Generation (ATCG) is an important topic in Software Testing, 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 ATCG techniques, much remains to be learned, however, about what makes a particular technique work well (or not) for a specific software system. Therefore, we propose a new methodology to evaluate and select the most effective ATCG technique using structure-based complexity measures. Empirical tests are going to be performed using two different techniques: Search-based Software Testing (SBST) and Random Testing (RT).

Original languageEnglish
Title of host publicationProceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis
EditorsKoushik Sen
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages440-443
Number of pages4
ISBN (Electronic)9781450350761
DOIs
Publication statusPublished - Jul 2017
EventInternational Symposium on Software Testing and Analysis 2017 - Santa Barbara, United States of America
Duration: 10 Jul 201714 Jul 2017
Conference number: 26th
https://conf.researchr.org/home/issta-2017 (Website)
https://dl-acm-org.ezproxy.lib.monash.edu.au/doi/proceedings/10.1145/3092703#issue-downloads (Proceedings)

Conference

ConferenceInternational Symposium on Software Testing and Analysis 2017
Abbreviated titleISSTA 2017
CountryUnited States of America
CitySanta Barbara
Period10/07/1714/07/17
Internet address

Keywords

  • Automated Software Testing
  • META Framework
  • RT
  • SBST

Cite this

Oliveira, C. (2017). Mapping hardness of Automated Software Testing. In K. Sen (Ed.), Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis (pp. 440-443). Association for Computing Machinery (ACM). https://doi.org/10.1145/3092703.3098241