All Stories

  1. Report from the ASE 2024 Workshop on Replications and Negative Results
  2. The Case for Compact AI
  3. Model Review: A PROMISEing Opportunity
  4. Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project Health
  5. One way to build faster, more accurate, and explainable Data-lite Machine Learning models
  6. Fair Enough: Searching for Sufficient Measures of Fairness
  7. Methods for stabilizing models across large samples of projects (with case studies on predicting defect and project health)
  8. How to improve deep learning for software analytics
  9. Dazzle
  10. Lessons learned from hyper-parameter tuning for microservice candidate identification
  11. FRUGAL: Unlocking Semi-Supervised Learning for Software Analytics
  12. Documenting evidence of a replication of ‘populating a release history database from version control and bug tracking systems’
  13. Documenting evidence of a reuse of ‘on the number of linear regions of deep neural networks’
  14. Documenting evidence of a reuse of ‘a systematic study of the class imbalance problem in convolutional neural networks’
  15. Documenting evidence of a reuse of ‘“why should I trust you?”: explaining the predictions of any classifier’
  16. Documenting evidence of a replication of ‘analyze this! 145 questions for data scientists in software engineering’
  17. Documenting evidence of a reuse of ‘what is a feature? a qualitative study of features in industrial software product lines’
  18. Documenting evidence of a reuse of ‘RefactoringMiner 2.0’
  19. Documenting evidence of a reuse of ‘a systematic literature review of techniques and metrics to reduce the cost of mutation testing’
  20. Documenting evidence of a reproduction of ‘is there a “golden” feature set for static warning identification? — an experimental evaluation’
  21. Bias in machine learning software: why? how? what to do?
  22. Early Life Cycle Software Defect Prediction. Why? How?
  23. Structuring a Comprehensive Software Security Course Around the OWASP Application Security Verification Standard
  24. Making fair ML software using trustworthy explanation
  25. Fairway: a way to build fair ML software
  26. iSENSE2.0
  27. What disconnects practitioner belief and empirical evidence?