ABSTRACT
Complementarity between activities reveals that doing any one of them increases the returns to doing the others. In other words, complementarity leads to the synergistic effect that the whole is greater than the sum of its parts. Identifying and exploiting complementarity can benefit many cybernetic activities, where human-machine interactions are inherent and dominant. One such activity is requirements tracing that helps stakeholders to track the status of their goals. Although various kinds of support for human analysts in requirements tracing have been proposed, little is known about the nature of complementarity when different tracing practices are involved. In this paper, we explore the role of complementarity by considering together the tagging-to-trace (T2T) and learning-to-trace (L2T) activities. We present a novel approach to examining which T2T and L2T practices enhance the qualities of each other. Our approach also uncovers the environmental factors which the complementarity is sensitive to. Applying our approach to the logs of 140 analyst-tracing units offers operational insights into the rigorous detection of complementarity and shows the importance of understanding the cybernetic conditions under which the requirements tracing practices may in fact be complementary.
ABSTRACT
Developers often spend valuable time navigating and seeking relevant code in software maintenance. Currently, there is a lack of theoretical foundations to guide tool design and evaluation to best shape the code base to developers. This paper contributes a unified code navigation theory in light of the optimal food-foraging principles. We further develop a novel framework for automatically assessing the foraging mechanisms in the context of program investigation. We use the framework to examine to what extent the clustering of software entities affects code foraging. Our quantitative analysis of long-lived open-source projects suggests that clustering enriches the software environment and improves foraging efficiency. Our qualitative inquiry reveals concrete insights into real developer's behavior. Our research opens the avenue toward building a new set of ecologically valid code navigation tools.
ABSTRACT
Group size is a key factor in collaborative software development and many other cybernetic applications where task assignments are important. While methods exist to estimate its value for proprietary projects, little is known about how group size affects distributed and decentralized cybernetic applications and in particular open source software (OSS) development. This paper presents a novel approach in which we frame developers' collective resolution of OSS change tasks as a social information foraging problem. This new perspective enables us to predict the optimal group size and quantify group size's effect on individual performance. We test the theory with data mined from two projects: 1) Firefox and 2) Mylyn. This paper not only uncovers the mismatch of optimal and actual group sizes, but also reveals the association of optimality with improved productivity. In addition, the social-level productivity gain is observed as project evolves. We show this paper's impact by extending the frontiers of knowledge in two areas: 1) social coding and 2) recommendation systems.
