All Stories

  1. How do machine learning engineers see and manage sustainability?
  2. Advancing LLM-Based Issue Report Classification with Explained Few-Shot Learning, Intent Extraction, Ensemble, and Summarization
  3. What makes software engineers use LLMs? Usefulness and habit, not culture
  4. Causes, effects, and fixes for collaboration problems in ML projects
  5. Bots in software development: benefits, challenges, and good practices
  6. FRINGE: context-aware FaiRness engineerING in complex software systEms
  7. Early and Realistic Exploitability Prediction of Just-Disclosed Software Vulnerabilities: How Reliable Can It Be?
  8. Using Genetic Algorithms to Improve Security Testing for IoT Devices.
  9. Security Risk Assessment on Cloud: A Systematic Mapping Study
  10. Learning to manage software project risks by playing a game
  11. Cultural Differences in Software Development
  12. Impact of culture on community smells in software development teams
  13. Toward static test flakiness prediction: a feasibility study
  14. Testing of Mobile Applications in the Wild
  15. Refactoring community smells in the wild
  16. Comparing the effectiveness of three parallelisation approaches for genetic algorithms
  17. Do developers update third-party libraries in mobile apps?
  18. A parallel genetic algorithms framework based on Hadoop MapReduce
  19. Exploiting prior-phase effort data to estimate the effort for the subsequent phases
  20. Web effort estimation