Automatic Bias Detection in Source Code Review

Abstract: Bias is an inherent threat to human decision-making, including in decisions made during software development. Extensive research has demonstrated the presence of biases at various stages of the software development life-cycle. Notably, code reviews are highly susceptible to prejudice-induced biases, and individuals are often unaware of these biases as they occur. Developing methods to automatically detect these biases is crucial for addressing the associated challenges. Recent advancements in visual data analytics have shown promising results in detecting potential biases by analyzing user interaction patterns. In this project, we propose a controlled experiment to extend this approach to detect potentially biased outcomes in code reviews by observing how reviewers interact with the code. We employ the "spotlight model of attention", a cognitive framework where a reviewer's gaze is tracked to determine their focus areas on the review screen. This focus, identified through gaze tracking, serves as an indicator of the reviewer's areas of interest or concern. We plan to analyze the sequence of gaze focus using advanced sequence modeling techniques, including Markov Models, Recurrent Neural Networks (RNNs), and Conditional Random Fields (CRF). These techniques will help us identify patterns that may suggest biased interactions. We anticipate that the ability to automatically detect potentially biased interactions in code reviews will significantly reduce unnecessary push-backs, enhance operational efficiency, and foster greater diversity and inclusion in software development. This approach not only helps in identifying biases but also in creating a more equitable development environment by mitigating these biases effectively

Automatic Bias Detection in Source Code Review

The paper titled "Automatic Bias Detection in Source Code Review" explores the emerging domain of cognitive bias identification within software engineering processes, specifically focusing on source code reviews. Recognizing that biases represent a significant obstacle to rational decision-making, the authors aim to highlight how such biases can permeate code review processes and suggest methodologies to counteract their influence.

Objective and Methodology

This research seeks to implement a controlled experiment to detect biases in code reviews automatically, using advanced interaction and gaze tracking methodologies. By harnessing the spotlight model of attention—tracking reviewer engagement patterns with code segments—the study uses gaze data as a proxy for the focus areas of concern. The authors apply advanced sequence modeling techniques like Markov Models, Recurrent Neural Networks (RNNs), and Conditional Random Fields (CRF) to classify interaction sequences that could predict biased decision-making patterns.

Implications and Numerical Insights

The numerical findings from experiments examining code reviews substantiate the notion that cognitive biases, including gender, ethnicity, and age-related biases, impact review processes significantly. For instance, Murphy-Hill et al.'s related study noted biases correlating with demographic data, estimating significant time costs due to biased pushbacks. Furthermore, Ford et al.'s eye-tracking research revealed reviewers often fixated more on social indicators than self-reported, implying potential biases that extend beyond the code itself.

By automating bias detection, this study anticipates more fair and efficient code review processes, potentially saving substantial programming time and enhancing inclusivity. The broad implications suggest that operationalizing such methodologies could transform collaborative software development by curtailing cognitive biases' influence.

Literature Contextualization

The authors ground their work within an extensive literature on cognitive biases in software engineering, citing key studies identifying prevalent biases and their effects on decision-making efficacy. Particularly, Mohanani et al. catalogued numerous biases active across the Software Development Life Cycle (SDLC). Experiments conducted by Chattopadhyay et al. highlight the frequency and impact of these biases on developer actions, reinforcing the necessity for systematic bias-detection frameworks.

Future Directions

Moving forward, researchers are encouraged to refine the gaze-tracking methodologies employed, potentially integrating multi-modal data sources for richer, more predictive models. The proposed methodologies could see enhancements through real-time bias mitigation strategies embedded within development environments, including real-time feedback mechanisms highlighting potential biases in reviewers' gaze patterns.

Conclusion

This study offers a compelling analysis into the automation of bias detection within software development, addressing a crucial facet of cognitive bias research. By advancing this domain through rigorous experimental design and data analytics, the authors provide a framework essential for enhancing software engineering processes' objectivity and inclusivity. The promising methodologies presented anticipate meaningful developments in AI and machine learning's capacity to address and mitigate cognitive biases within technical domains. These insights could pave the way for widespread bias-detection adoption, fostering more equitable and efficient software development practices globally.