ABSTRACT Context With the fast advancement of techniques in artificial intelligence (AI) and of the target infrastructures in the last decades, AI software is becoming an undeniable part of software system projects. As in most cases in history, however, development methods and guides follow the advancements in technology with phase differences. Purpose With an aim to elicit and integrate available evidence from AI software development practices into a process model, this study synthesizes the contributions of the validation studies reported in scientific literature. Method We applied a systematic literature review to retrieve, select, and analyze the primary studies. After a comprehensive and rigorous search and scoping review, we identified 82 studies that make various contributions in relation to AI software development practices. To increase the effectiveness of the synthesis and the usefulness of the outcome, for detailed analysis, we selected 14 primary studies (out of 82) that empirically validated their contributions. Results We carefully reviewed the selected studies that validate proposals on approaches/models, methods/techniques, tasks/phases, lessons learned/best practices, or workflows. We mapped the steps/activities in these proposals with the knowledge areas in SWEBOK, and using the evidence in this mapping and the primary studies, we synthesized a process model that integrates activities, artifacts, and roles for AI‐enabled software system development. Conclusion To the best of our knowledge, this is the first study that proposes such a process model by eliciting and gathering the contributions of the validation studies in a bottom‐up manner. We expect that the output of this synthesis will be input for further research to validate or improve the process model.

Code reviews, while effective, can be crippled by process smells if not performed correctly. A typical process smell that harms the efficacy of code reviews is the ‘Looks Good To Me’ (LGTM) smell, wherein a reviewer approves a code review task without reviewing the code attentively. Low-quality code reviews can be harmful, as they can cause bugs to slip into a product codebase leading to potentially severe consequences. In this paper, we propose an innovative solution to potentially minimize the occurrence of the LGTM smell commonly found in code reviews. We built a tool that is a proof-of-concept implementation of our solution, which incorporates the concept of mutation testing into code reviews. It provides a platform where pull request authors can apply mutations to the pull request code in GitHub. Reviewer attention and review efficacy are measured based on their mutation score. To the best of our knowledge, our proof of concept implementation is the first-ever code review tool that uses the concept of mutation testing. We validated our proposed solution with eight developers and received promising results.