Analogy-based software development effort estimation: A systematic mapping and review

Context: Analogy-based software development effort estimation (ASEE) techniques have gained considerable attention from the software engineering community. However, to our knowledge, no systematic mapping has been created of ASEE studies and no review has been carried out to analyze the empirical evidence on the performance of ASEE techniques. Objective: The objective of this research is twofold: (1) to classify ASEE papers according to five criteria: research approach, contribution type, techniques used in combination with ASEE methods, and ASEE steps, as well as identifying publication channels and trends; and (2) to analyze these studies from five perspectives: estimation accuracy, accuracy comparison, estimation context, impact of the techniques used in combination with ASEE methods, and ASEE tools. Method: We performed a systematic mapping of ASEE studies published in the period 1990–2012, and reviewed them based on an automated search of four electronic databases. Results: In total, we identified 65 studies published between 1990 and 2012, and classified them based on our predefined classification criteria. The mapping study revealed that most researchers focus on addressing problems related to the first step of an ASEE process, that is, feature and case subset selection. The results of our detailed analysis show that ASEE methods outperform the eight techniques with which they were compared, and tend to yield acceptable results especially when combining ASEE techniques with fuzzy logic (FL) or genetic algorithms (GA). Conclusion: Based on the findings of this study, the use of other techniques such FL and GA in combination with an ASEE method is promising to generate more accurate estimates. However, the use of ASEE techniques by practitioners is still limited: developing more ASEE tools may facilitate the application of these techniques and then lead to increasing the use of ASEE techniques in industry. 2014 Elsevier B.V. All rights reserved.