Using a Balanced Scorecard to Identify Opportunities to Improve Code Review Effectiveness: An Industrial Experience Report
Not on the map. Why?
Abstract (arXiv)
Peer code review is a widely adopted software engineering practice to ensure code quality and ensure software reliability in both the commercial and open-source software projects. Due to the large effort overhead associated with practicing code reviews, project managers often wonder, if their code reviews are effective and if there are improvement opportunities in that respect. Since project managers at Samsung Research Bangladesh (SRBD) were also intrigued by these questions, this research developed, deployed, and evaluated a production-ready solution using the Balanced SCorecard (BSC) strategy that SRBD managers can use in their day-to-day management to monitor individual developer's, a particular project's or the entire organization's code review effectiveness. Following the four-step framework of the BSC strategy, we: 1) defined the operation goals of this research, 2) defined a set of metrics to measure the effectiveness of code reviews, 3) developed an automated mechanism to measure those metrics, and 4) developed and evaluated a monitoring application to inform the key stakeholders. Our automated model to identify useful code reviews achieves 7.88% and 14.39% improvement in terms of accuracy and minority class F1 score respectively over the models proposed in prior studies. It also outperforms human evaluators from SRBD, that the model replaces, by a margin of 25.32% and 23.84% respectively in terms of accuracy and minority class F1 score. In our post-deployment survey, SRBD developers and managers indicated that they found our solution as useful and it provided them with important insights to help their decision makings.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Jan 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 1; 2025: 1; 2026: 2.
4 papers.
- 2024Leveraging Reviewer Experience in Code Review Comment Generationcited by 4
- 2026Rethinking Code Review in the Age of AI: A Vision for Agentic Code Reviewcited by 1
- 2026Is Agentic Code Review Helpful? Mining Developers' Feedback to CodeRabbit Reviews in the Wildcited by 1
- 2025Exploring the Potential of Large Language Models in Fine-Grained Review Comment Classificationcited by 0
- 2026Is Agentic Code Review Helpful? Mining Developers' Feedback to CodeRabbit Reviews in the Wild3 Jul 2026
- 2026Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review17 May 2026
- 2025Exploring the Potential of Large Language Models in Fine-Grained Review Comment Classification13 Aug 2025
- 2024Leveraging Reviewer Experience in Code Review Comment Generation17 Sep 2024