Related papers: Automatic Bias Detection in Source Code Review

Cognitive biases appear during code review. They significantly impact the creation of feedback and how it is interpreted by developers. These biases can lead to illogical reasoning and decision-making, violating one of the main hypotheses…

Cognitive biases are mental shortcuts humans use in dealing with information and the environment, and which result in biased actions and behaviors (or, actions), unbeknownst to themselves. Biases take many forms, with cognitive biases…

This paper proposes an open source visual analytics tool consisting of several views and perspectives on eye movement data collected during code reading tasks when writing computer programs. Hence the focus of this work is on code and…

While decision makers have begun to employ machine learning, machine learning models may make predictions that bias against certain demographic groups. Semi-automated bias detection tools often present reports of automatically-detected…

Code reviews are a critical aspect of open-source software (OSS) development, ensuring quality and fostering collaboration. This study examines perceptions, challenges, and biases in OSS code review processes, focusing on the perspectives…

Bias in web search has been in the spotlight of bias detection research for quite a while. At the same time, little attention has been paid to query suggestions in this regard. Awareness of the problem of biased query suggestions has been…

Program comprehension is a dominant process in software development and maintenance. Experts are considered to comprehend the source code efficiently by directing their gaze, or attention, to important components in it. However, reflecting…

Many software projects employ manual code review to gatekeep defects and vulnerabilities in the code before integration. However, reviewers often work under time pressure and rely primarily on static inspection, leaving the dynamic aspects…

Recognizing, assessing, countering, and mitigating the biases of different nature from heterogeneous sources is a critical problem in designing a cognitive Decision Support System (DSS). An example of such a system is a cognitive…

Machine learning systems are increasingly deployed in high-stakes domains, yet they remain vulnerable to bias systematic disparities that disproportionately impact specific demographic groups. Traditional bias detection methods often depend…

Code review is a well-established and valued practice in the software engineering community contributing to both code quality and interpersonal benefits. However, there are challenges in both tools and processes that give rise to…

Context: Contemporary software development organizations lack diversity and the ratios of women in Free and open-source software (FOSS) communities are even lower than the industry average. Although the results of recent studies hint the…

Cognitive biases, systematic deviations from rationality in judgment, pose significant challenges in generating objective content. This paper introduces a novel approach for real-time cognitive bias detection in user-generated text using…

Background: Contemporary software development organizations lack diversity and the ratios of women in Free and open-source software (FOSS) communities are even lower than the industry average. Although the results of recent studies hint the…

As a direct cause of software defects, human error is the key to understanding and identifying defects. We propose a new code inspection method: targeted code inspection based on human error mechanisms of software engineers. Based on the…

As the adoption of LLMs becomes more widespread in software coding ecosystems, a pressing issue has emerged: does the generated code contain social bias and unfairness, such as those related to age, gender, and race? This issue concerns the…

Low-code applications are gaining popularity across various fields, enabling non-developers to participate in the software development process. However, due to the strong reliance on graphical user interfaces, they may unintentionally…

Bias analysis for synthetic face detection is bound to become a critical topic in the coming years. Although many detection models have been developed and several datasets have been released to reliably identify synthetic content, one…

The awareness and mitigation of biases are of fundamental importance for the fair and transparent use of contextual language models, yet they crucially depend on the accurate detection of biases as a precursor. Consequently, numerous bias…

Mixed-initiative visual analytics systems incorporate well-established design principles that improve users' abilities to solve problems. As these systems consider whether to take initiative towards achieving user goals, many current…