Investigating the Social Representations of Code Smell Identification: A Preliminary Study

• Published in: 2019 IEEE/ACM 12th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE) pp. 53 - 60
• Main Authors: Maiani de Mello, Rafael, Goncalves Uchoa, Anderson, Felicio Oliveira, Roberto, Tenorio Martins de Oliveira, Daniel, Fonseca, Baldoino, Fabricio Garcia, Alessandro, de Barcellos de Mello, Fernanda
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-05-2019
• Subjects: code smells; Companies; Industries; Instruments; qualitative research; social representation; Software; Software engineering; Task analysis
• DOI: 10.1109/CHASE.2019.00022
• EISSN: 2574-1837

Context: The identification of code smells is one of the most subjective tasks in software engineering. A key reason is the influence of collective aspects of communities working on this task, such as their beliefs regarding the relevance of certain smells. However, collective aspects are often neglected in the context of smell identification. For this purpose, we can use the social representations theory. Social representations comprise the set of values, behaviors and practices of communities associated with a social object, such as the task of identifying smells. Aim: To characterize the social representations behind smell identification. Method: We conducted a preliminary study on the social representations of smell identification by two communities. One community is composed of postgraduate students involved in various investigations related to code smells. The other community is composed of practitioners from industry, with experience in code reviews. We analyzed the associations made by the study participants about smell identification, i.e., what immediately comes to their minds when they think about this task. Results: One of the key findings is that only the community of practitioners strongly associates this task with semantic smells. This finding suggests research directions on code smells may be revisited, as they focus mostly on measurable or structural smells. Considering the novelty of using the social representations theory in software engineering, we also compiled a set of lessons learned. For instance, we observed some key challenges we faced in using the theory. These challenges include: (i) the predominance of associations with technical rather than non-technical concepts, and (ii) the fuzzy definitions of key concepts in our field. Conclusion: We found initial evidence that social representations analysis is a useful instrument to reveal discrepancies and commonalities on how different communities deal with a subjective task. Thus, we expect the experience reported in this paper may encourage and contribute to future studies of social representations in the field.