Software bots automate tasks within Open Source Software (OSS) projects’ pull requests and save reviewing time and effort (“the good”). However, their interactions can be disruptive and noisy and lead to information overload (“the bad”). To identify strategies to overcome such problems, we applied Design Fiction as a participatory method with 32 practitioners. We elicited 22 design strategies for a bot mediator or the pull request user interface (“the promising”). Participants envisioned a separate place in the pull request interface for bot interactions and a bot mediator that can summarize and customize other bots’ actions to mitigate noise. We also collected participants’ perceptions about a prototype implementing the envisioned strategies. Our design strategies can guide the development of future bots and social coding platforms. @en

Software bots have been facilitating several development activities
in Open Source Software (OSS) projects, including code review. However,
these bots may bring unexpected impacts to group dynamics, as frequently
occurs with new technology adoption. Understanding and anticipating such
effects is important for planning and management. To analyze these effects, we
investigate how several activity indicators change after the adoption of a code
review bot. We employed a regression discontinuity design on 1,194 software
projects from GitHub. We also interviewed 12 practitioners, including open-
source maintainers and contributors. Our results indicate that the adoption
of code review bots increases the number of monthly merged pull requests,
decreases monthly non-merged pull requests, and decreases communication
among developers. From the developers’ perspective, these effects are explained
by the transparency and confidence the bot comments introduce, in addition
to the changes in the discussion focused on pull requests. Practitioners and
maintainers may leverage our results to understand, or even predict, bot effects
on their projects.@en

Past researchs have been trying to recommend artifacts that are likely to change together in a task to assist developers in making changes to a software system, often using techniques like association rules. Association rules learning is a data mining technique that has been frequently used to discover evolutionary couplings. These couplings constitute a fundamental piece of modern change prediction techniques. However, using association rules to detect evolutionary coupling requires a number of configuration parameters, such as measures of interest (e.g. support and confidence), their cut-off values, and the portion of the commit history from which co-change relationships will be extracted. To accomplish this set up, researchers have to carry out empirical studies for each project, testing a few variations of the parameters before choosing a configuration. This makes it difficult to use association rules in practice, since developers would need to perform experiments before applying the technique and would end up choosing non-optimal solutions that lead to wrong predictions. In this paper, we propose a fitness function for a Genetic Algorithm that optimizes the co-change recommendations and evaluate it on five open source projects (CPython, Django, Laravel, Shiny and Gson). The results indicate that our genetic algorithm is able to find optimized cut-off values for support and confidence, as well as to determine which length of commit history yields the best recommendations. We also find that, for projects with less commit history (5k commits), our approach produced better results than the regression function proposed in the literature. This result is particularly encouraging, because repositories such as GitHub host many young projects. Our results can be used by researchers when conducting co-change prediction studies and by tool developers to produce automated support to be used by practitioners.@en