Multi-objective optimization for software testing effort estimation

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

10 Scopus Citations
Scopus Metrics
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
Place of PublicationUSA
PublisherKnowledge Systems Institute Graduate School
Pages527-530
Volume2016-January
ISBN (print)189170639, 9781891706394
Publication statusPublished - Jul 2016

Publication series

NameSEKE
Volume2016
ISSN (Print)2325-9000
ISSN (electronic)2325-9086

Conference

Title28th International Conference on Software Engineering and Knowledge Engineering, SEKE 2016
PlaceUnited States
CityRedwood City
Period1 - 3 July 2016

Link(s)

DOI

DOI
Check@CityULib
Document Link

Links
Link to Scopus https://www.scopus.com/record/display.uri?eid=2-s2.0-84988385222&origin=recordpage
Permanent Link https://scholars.cityu.edu.hk/en/publications/publication(c4cab603-6004-4544-afab-8e9d527a7a5d).html

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.

Research Area(s)

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

Fingerprint

Publication fingerprints text

100
Software Testing Computer Science
100
Multi-Objective Opt... Computer Science
100
Testing Effort Computer Science
100
Effort Estimation Computer Science
100
testing INIS
100
computer codes INIS
100
optimization INIS
100
Multi-objective Opt... Keyphrases
100
Test Effort Estimat... Keyphrases
100
Software Testing Ef... Keyphrases
Full Fingerprint ›

Citation Format(s)

[ RIS ] [ BibTeX ]
Multi-objective optimization for software testing effort estimation. / Mensah, Solomon; Keung, Jacky; Bennin, Kwabena Ebo et al.
Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE. Vol. 2016-January USA: Knowledge Systems Institute Graduate School, 2016. p. 527-530 163 (SEKE; Vol. 2016).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review