کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
495094 | 862815 | 2015 | 8 صفحه PDF | دانلود رایگان |
• The GSD team-level service climate is one of the key determinants to achieve the outcome of global software development (GSD) projects.
• To evaluate the GSD team-level service climate and GSD project outcome relationship based on Adaptive Neuro-Fuzzy Inference System (ANFIS) with the Hybrid Taguchi-Genetic Learning Algorithm (HTGLA).
• The applicability and capability of HTGLA-based ANFIS approach is investigated through the real data sets obtained from Indian software industries.
The GSD team-level service climate is one of the key determinants to achieve the outcome of global software development (GSD) projects from the software service outsourcing perspective. The main aim of this study is to evaluate the GSD team-level service climate and GSD project outcome relationship based on adaptive neuro-fuzzy inference system (ANFIS) with the genetic learning algorithm. For measuring the team-level service climate, the Hybrid Taguchi-Genetic Learning Algorithm (HTGLA) is adopted in the ANFIS, which is more appropriate to determine the optimal premise and consequent constructs by reducing the root-mean-square-error (RMSE) of service climate criteria. For measuring the GSD team-level service climate, synthesizing the literature reviews and consistent with the earlier studies on IT service climate which is classified into three main criterion: managerial practices (deliver quality of service), global service climate (measure overall perceptions), service leadership (goal setting, work planning, and coordination) which comprises 25 GSD team-level service climate attributes. The experimental results show that the optimal prediction error is obtained by the HTGLA-based ANFIS approach is 3.26%, which outperforms the earlier result that is the optimal prediction errors 4.41% and 5.75% determined, respectively, by ANFIS and statistical methods.
ANFIS architecture for a multi-inputs and single output Sugeno model with fuzzy n rules.Figure optionsDownload as PowerPoint slide
Journal: Applied Soft Computing - Volume 30, May 2015, Pages 628–635