TY - GEN
T1 - Multi-objective optimization for software testing effort estimation
AU - Mensah, Solomon
AU - Keung, Jacky
AU - Bennin, Kwabena Ebo
AU - Bosu, Michael Franklin
PY - 2016/7
Y1 - 2016/7
N2 - 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.
AB - 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.
KW - Cross-company
KW - Deep neural networks
KW - Optimization
KW - Software testing effort
KW - Within-company
UR - https://www.scopus.com/pages/publications/84988385222
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84988385222&origin=recordpage
U2 - 10.18293/SEKE2016-017
DO - 10.18293/SEKE2016-017
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 189170639
SN - 9781891706394
VL - 2016-January
T3 - SEKE
SP - 527
EP - 530
BT - Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
PB - Knowledge Systems Institute Graduate School
CY - USA
T2 - 28th International Conference on Software Engineering and Knowledge Engineering, SEKE 2016
Y2 - 1 July 2016 through 3 July 2016
ER -