Footprints of fitness functions in Search-Based Software Testing

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Abstract

Testing is technically and economically crucial for ensuring software quality. One of the most challenging testing tasks is to create test suites that will reveal potential defects in software. However, as the size and complexity of software systems increase, the task becomes more labour-intensive and manual test data generation becomes infeasible. To address this issue, researchers have proposed different approaches to automate the process of generating test data using search techniques; an area that is known as Search-Based Software Testing (SBST). SBST methods require a fitness function to guide the search to promising areas of the solution space. Over the years, a plethora of fitness functions have been proposed. Some methods use control information, others focus on goals. Deciding on what fitness function to use is not easy, as it depends on the software system under test. This work investigates the impact of software features on the effectiveness of different fitness functions. We propose the Mapping the Effectiveness of Test Automation (META) Framework which analyses the footprint of different fitness functions and creates a decision tree that enables the selection of the appropriate function based on software features.

Original languageEnglish
Title of host publicationGECCO'19 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference
EditorsAnne Auger, Thomas Stützle
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages1399-1407
Number of pages9
ISBN (Electronic)9781450361118
DOIs
Publication statusPublished - 2019
EventThe Genetic and Evolutionary Computation Conference 2019 - Prague, Czech Republic
Duration: 13 Jul 201917 Jul 2019
https://gecco-2019.sigevo.org/index.html/HomePage

Conference

ConferenceThe Genetic and Evolutionary Computation Conference 2019
Abbreviated titleGECCO 2019
CountryCzech Republic
CityPrague
Period13/07/1917/07/19
OtherGECCO is the largest selective conference in the field of Evolutionary Computation, and the main conference of the Special Interest Group on Genetic and Evolutionary Computation (SIGEVO) of the Association for Computing Machinery (ACM). GECCO implements a rigorous and selective reviewing process to identify important and technically sound papers to publish. The technical program is divided into thirteen tracks reflecting all aspects of our field and chaired by experts who make the decisions on accepted papers.
Internet address

Keywords

  • Genetic Algorithms
  • Search Based Software Engineering

Cite this

Oliveira, C., Li, Y. F., Aleti, A., & Abdelrazek, M. (2019). Footprints of fitness functions in Search-Based Software Testing. In A. Auger, & T. Stützle (Eds.), GECCO'19 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference (pp. 1399-1407). Association for Computing Machinery (ACM). https://doi.org/10.1145/3321707.3321880