Applications of case‐based reasoning in Software Engineering: a systematic mapping study

Abstract

Domain knowledge for various decision-making activities of Software Engineering (SE) is rarely available in a structured or well-formalised form. Owing to lack of the well-informed knowledge, decision making for different kinds of predictions and estimations in SE domain is a challenge. Maintenance and elicitation of domain knowledge is an overwhelming task and causes the knowledge acquisition bottleneck. Most of the artificial intelligence techniques of prediction and estimation do not work in absence of complete and structured knowledge. Case-based reasoning (CBR) is a lazy learning paradigm of artificial intelligence which takes care of this challenge and helps to reduce the knowledge availability bottleneck. This technique exploits the similar experience of past which may be available in unstructured form, and improves its learning curve with passage of time. In literature, CBR has been successfully applied in various areas of SE, but there is lack of single systematic panoramic picture which might have highlighted the potential research questions in this direction. In this study, the author has presented a comprehensive and panoramic systematic mapping study of various CBR applications in SE domain, and identified some promising future research directions.

Hybrid case-base maintenance approach for modeling large scale case-based reasoning systems

“…For the purpose of solving complex and complicated problems, the applications of CBR are widely distributed directly or indirectly in almost every field of knowledge. It has been effectively used for adoption problem in medical CBR systems [5], e-commerce [39], data mining [25], autonomic systems [19], travel planning [6], software engineering [7,18] and an enormous range of other applications [34,48]. Furthermore, CBR can also be used to mine big and sparse data as well [37].…”