Cognitive Biases in Software Engineering: A Systematic Mapping Study
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Abstract (arXiv)
One source of software project challenges and failures is the systematic errors introduced by human cognitive biases. Although extensively explored in cognitive psychology, investigations concerning cognitive biases have only recently gained popularity in software engineering (SE) research. This paper therefore systematically maps, aggregates and synthesizes the literature on cognitive biases in software engineering to generate a comprehensive body of knowledge, understand state of the art research and provide guidelines for future research and practise. Focusing on bias antecedents, effects and mitigation techniques, we identified 65 articles, which investigate 37 cognitive biases, published between 1990 and 2016. Despite strong and increasing interest, the results reveal a scarcity of research on mitigation techniques and poor theoretical foundations in understanding and interpreting cognitive biases. Although bias-related research has generated many new insights in the software engineering community, specific bias mitigation techniques are still needed for software professionals to overcome the deleterious effects of cognitive biases on their work.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Jul 2017.
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Citing papers per year: 2023: 4; 2024: 2; 2025: 1; 2026: 2.
9 papers.
- 2023Bias and Fairness in Chatbots: An Overviewcited by 20
- 2023GenAI Mirage: The Impostor Bias and the Deepfake Detection Challenge in the Era of Artificial Illusionscited by 10
- 2023A Systematic Literature Review on Client Selection in Federated Learningcited by 3
- 2024InterEvo-TR: Interactive Evolutionary Test Generation With Readability Assessmentcited by 3
- 2024Rolling in the deep of cognitive and AI biasescited by 2
- 2025Is General-Purpose AI Reasoning Sensitive to Data-Induced Cognitive Biases? Dynamic Benchmarking on Typical Software Engineering Dilemmascited by 2
- 2026Mitigating Prompt-Induced Cognitive Biases in General-Purpose AI for Software Engineeringcited by 1
- 2023Introducing Construct Theory as a Standard Methodology for Inclusive AI Modelscited by 0
- 2026Words Speak Louder Than Code: Investigating Cognitive Heuristics in LLM-Based Code Vulnerability Detectioncited by 0
- 2026Words Speak Louder Than Code: Investigating Cognitive Heuristics in LLM-Based Code Vulnerability Detection29 Jun 2026
- 2026Mitigating Prompt-Induced Cognitive Biases in General-Purpose AI for Software Engineering18 Apr 2026
- 2025Is General-Purpose AI Reasoning Sensitive to Data-Induced Cognitive Biases? Dynamic Benchmarking on Typical Software Engineering Dilemmas15 Aug 2025
- 2024Rolling in the deep of cognitive and AI biases30 Jul 2024
- 2024InterEvo-TR: Interactive Evolutionary Test Generation With Readability Assessment13 Jan 2024
- 2023GenAI Mirage: The Impostor Bias and the Deepfake Detection Challenge in the Era of Artificial Illusions24 Dec 2023
- 2023Bias and Fairness in Chatbots: An Overview16 Sep 2023
- 2023A Systematic Literature Review on Client Selection in Federated Learning8 Jun 2023
- 2023Introducing Construct Theory as a Standard Methodology for Inclusive AI Models19 Apr 2023