Explainable AI for Software Engineering

Author:

Tantithamthavorn Chakkrit Kla,Jiarpakdee Jirayus

Publisher

IEEE

Reference17 articles.

1. An experience report on defect modelling in practice

2. Actionable Analytics: Stop Telling Me What It Is; Please Tell Me What To Do

3. The impact of mislabelling on the performance and interpretation of defect prediction models;tantithamthavorn;Proceedings of the International Conference on Software Engineering ICSE'94,2015

4. Automated parameter optimization of classification techniques for defect prediction models

5. An Empirical Comparison of Model Validation Techniques for Defect Prediction Models

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1. A Formal Explainer for Just-In-Time Defect Predictions;ACM Transactions on Software Engineering and Methodology;2024-08-26

2. Practitioners’ Challenges and Perceptions of CI Build Failure Predictions at Atlassian;Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering;2024-07-10

3. Studying the explanations for the automated prediction of bug and non-bug issues using LIME and SHAP;Empirical Software Engineering;2024-06-13

4. On the Reliability and Explainability of Language Models for Program Generation;ACM Transactions on Software Engineering and Methodology;2024-06-03

5. Explanation-driven Self-adaptation using Model-agnostic Interpretable Machine Learning;Proceedings of the 19th International Symposium on Software Engineering for Adaptive and Self-Managing Systems;2024-04-15

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