EkmEx - An Extended Framework for Labeling an Unlabeled Fault Dataset

Software fault prediction (SFP) is a quality assurance process that identifies if certain modules are faultprone (FP) or not-fault-prone (NFP). Hence, it minimizes the testing efforts incurred in terms of cost and time. Supervised machine learning techniques have capacity to spot-out the FP modules. However, such techniques require fault information from previous versions of software product. Such information, accumulated over the life-cycle of software, may neither be readily available nor reliable. Currently, clustering with experts’ opinions is a prudent choice for labeling the modules without any fault information. However, the asserted technique may not fully comprehend important aspects such as selection of experts, conflict in expert opinions, catering the diverse expertise of domain experts etc. In this paper, we propose a comprehensive framework namedEkmEx that extends the conventional fault prediction approaches while providing mathematical foundation through aspects not addressed so far. The EkmEx guides in selection of experts, furnishes an objective solution for resolve of verdictconflicts and manages the problem of diversity in expertise of domain experts. We performed expert-assisted module labeling through EkmEx and conventional clustering on seven public datasets of NASA. The empirical outcomes of research exhibit significant potential of the proposed framework in identifying FP modules across all seven dataset