Change Requests (CRs) are key elements to software maintenance and evolution. Finding the appropriate developer to a CR is crucial for obtaining the lowest, economically feasible, fixing time. Nevertheless, assigning CRs is a labor-intensive and time consuming task. In this paper, we present a semi-automated CR assignment approach which combine rule-based and information retrieval techniques. The approach emphasizes the use of contextual information, essential to effective assignments, and puts the development team in control of the assignment rules, toward making its adoption easier. Results of an empirical evaluation showed that the approach is up to 46,5% more accurate than approaches which rely solely on machine learning techniques.

ABSTRACT Software maintenance starts as soon as the first artifacts are delivered and is essential for the success of the software. However, keeping maintenance activities and their related artifacts on track comes at a high cost. In this respect, change request (CR) repositories are fundamental in software maintenance. They facilitate the management of CRs and are also the central point to coordinate activities and communication among stakeholders. However, the benefits of CR repositories do not come without issues, and commonly occurring ones should be dealt with, such as the following: duplicate CRs, the large number of CRs to assign, or poorly described CRs. Such issues have led researchers to an increased interest in investigating CR repositories, by considering different aspects of software development and CR management. In this paper, we performed a systematic mapping study to characterize this research field. We analyzed 142 studies, which we classified in two ways. First, we classified the studies into different topics and grouped them into two dimensions:challengesandopportunities. Second, the challenge topics were classified in accordance with an existing taxonomy for information retrieval models. In addition, we investigated tools and services for CR management, to understand whether and how they addressed the topics identified. Copyright © 2013 John Wiley & Sons, Ltd.

Context: Change Request (CR) repositories play an important role in the software maintenance and evolution process. Through a CR repository, software changes are reported and assigned to developers. Finding the appropriate developer to a CR is crucial for obtaining the lowest, economically feasible, fixing time. Nevertheless, assigning CRs is a labor-intensive and time consuming task. Although many work have proposed automated approaches for CR assignment, they have been implemented without investigating the fundamental aspects which characterize the task itself. Objective: This paper investigates the effort that is taken to assign CR to appropriate developers and identifies the fundamental aspects that characterize it, such as the strategies to perform the assignments and the complexity involved in them. Such investigation improves the current knowledge on the topic, providing researchers and practitioners with useful information towards developing effective solutions. Method: A survey was performed with software developers to understand CR assignment in the Brazilian Federal Organization for Data Processing. The questionnaire was composed of 38 questions, being them both open-ended and closed-ended. We analyzed the answers of 36 respondents. Results: We find that: there is a significant amount of time being spent on assignments (e.g., assigning 20 CRs can take up to 3.3 hours); there are many strategies used to assign CRs, which are complementary to those used in current automated solutions; and CR assignment is very complexity due to a process that requires cognitive abilities for information seeking, communication, and memorization. Conclusion: CR repositories are fundamental to software maintenance, however assigning CRs to developers is an expensive activity. Although we understand that fully and totally accurate automation of assignments is unlikely, further improvements on this direction are feasible and necessary to reduce costs. This way, this paper brings relevant findings to guide new research on automated CR assignment.