Multi-objective optimization for software testing effort estimation

Solomon Mensah, Jacky Keung, Kwabena Ebo Bennin, Michael Franklin Bosu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)

Abstract

Software Testing Effort (STE), which contributes about 25-40% of the total development effort, plays a significant role in software development. In addressing the issues faced by companies in finding relevant datasets for STE estimation modeling prior to development, cross-company modeling could be leveraged. The study aims at assessing the effectiveness of cross-company (CC) and within-company (WC) projects in STE estimation. A robust multi-objective Mixed-Integer Linear Programming (MILP) optimization framework for the selection of CC and WC projects was constructed and estimation of STE was done using Deep Neural Networks. Results from our study indicate that the application of the MILP framework yielded similar results for both WC and CC modeling. The modeling framework will serve as a foundation to assist in STE estimation prior to the development of new a software project.

Original languageEnglish
Title of host publicationProceedings - SEKE 2016
Subtitle of host publication28th International Conference on Software Engineering and Knowledge Engineering
PublisherKnowledge Systems Institute Graduate School
Pages527-530
Number of pages4
ISBN (Electronic)189170639X, 9781891706394
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event28th International Conference on Software Engineering and Knowledge Engineering, SEKE 2016 - Redwood City
Duration: 1 Jul 20163 Jul 2016

Publication series

NameProceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
Volume2016-January
ISSN (Print)2325-9000
ISSN (Electronic)2325-9086

Conference

Conference28th International Conference on Software Engineering and Knowledge Engineering, SEKE 2016
Country/TerritoryUnited States
CityRedwood City
Period1/07/163/07/16

Keywords

  • Cross-company
  • Deep neural networks
  • Optimization
  • Software testing effort
  • Within-company

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