A fuzzy analytical hierarchy process to prioritize the success factors of requirement change management in global software development

Abstract

Planning and managing of requirement change management (RCM) process in global software development (GSD) are a complicated task, but the RCM plays a significant role in developing the quality software within time and budget. The key aim of this study is to prioritize the factors that could positively influence the RCM program in GSD context. To achieve the study objective, the questionnaire survey study was conducted to get the feedback of the practitioners concerning the success factors of RCM in GSD context. Moreover, the fuzzy analytical hierarchy process (FAHP) was applied. The application of FAHP is novel in this research domain as it has been effectively applied previously in various other research areas, for example, supplier selection, electronics and electrical, personnel selection, and agile software development. The results of this study will provide the prioritization‐based taxonomy of RCM success factors and also contribute by introducing the novel FAHP approach in the research domain of RCM in GSD. The FAHP approach assists the practitioners to reduce the uncertainty and vague opinions of RCM experts.

Success factors analysis for requirement elicitation in global software development paradigm: An empirical study

“…In many studies, authors proposed RCM frameworks to handle changes requests in GSD successfully. 96,98,99 • The use of suitable elicitation technique favors better elicitation process in GSD. In GSD, applying traditional elicitation techniques are very difficult.…”

Multicriteria decision making taxonomy of code recommendation system challenges: a fuzzy-AHP analysis

“…Similarly, Shameem et al [47] taxonomies the factors that could influence the agile processes in geographically distributed environment. Moreover, Akbar et al [3] prioritize the DevOps challenging factors using fuzzy AHP. Based on the above discussion, we could justify the application of fuzzy AHP method for this research study.…”

Prioritization Based Taxonomy of DevOps Challenges Using Fuzzy AHP Analysis

“…Hence, the classical AHP has some limitations due to the implementing the AHP in the Crisp environment, "Judgmental scale is unbalanced", and the "lack of ambiguity", "selection of judgment" are subjective. Though, fuzzy AHP is an updated version of AHP and that was develop to fix the uncertainties more effectively [50,51]. The fuzzy AHP is effective to address the uncertainty and "imprecise judgment of experts by handling the linguistic variables."…”

“…It is noted that the existing studies used the small of data size for fuzzy AHP analysis. In example, Akbar et al [51] performed the fuzzy AHP analysis considering the data collected form 23 experts. [57]used the opinion of 5 experts for perfume AHP analysis.…”