All Stories

  1. BayesInsights: Modelling Software Delivery and Developer Experience with Bayesian Networks at Bloomberg
  2. Industrial Deployment of an AI Multi-Agent System for Requirements-Driven Code Verification
  3. A Mixed-Method Study of Hot Fixing in Industry: Practices, Bottlenecks, and Opportunities
  4. It Is Giving Major Satisfaction: Why Fairness Matters for Software Practitioners
  5. Generative ai for testing of autonomous driving systems: A survey
  6. Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instances
  7. Hot Fixing Software: A Comprehensive Review of Terminology, Techniques, and Applications
  8. Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices
  9. Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices
  10. LLM-Based Misconfiguration Detection for AWS Serverless Computing
  11. Behind the Hot Fix: Demystifying Hot Fixing Industrial Practices at Zühlke and Beyond
  12. Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software
  13. SCOPE : Performance Testing for Serverless Computing
  14. Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems
  15. Game Software Engineering: A Controlled Experiment Comparing Automated Content Generation Techniques
  16. Broken Agreement: The Evolution of Solidity Error Handling
  17. Understanding Fairness in Software Engineering: Insights from Stack Exchange Sites
  18. Exploring LLM-Driven Explanations for Quantum Algorithms
  19. Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement
  20. Speeding up Genetic Improvement via Regression Test Selection
  21. The Patch Overfitting Problem in Automated Program Repair: Practical Magnitude and a Baseline for Realistic Benchmarking
  22. Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
  23. Fairness Testing: A Comprehensive Survey and Analysis of Trends
  24. TrickyBugs: A Dataset of Corner-case Bugs in Plausible Programs
  25. Greenlight: Highlighting TensorFlow APIs Energy Footprint
  26. User-Centric Deployment of Automated Program Repair at Bloomberg
  27. Fairness Improvement with Multiple Protected Attributes: How Far Are We?
  28. Assess and Summarize: Improve Outage Understanding with Large Language Models
  29. MEG: Multi-objective Ensemble Generation for Software Defect Prediction (HOP GECCO'23)
  30. Multi-objective Search for Gender-fair and Semantically Correct Word Embeddings (HOP GECCO'23)
  31. Who Judges the Judge: An Empirical Study on Online Judge Tests
  32. Automated Optimisation of Modern Software System Properties
  33. A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers
  34. MAAT: a novel ensemble approach to addressing fairness and performance bugs for machine learning software
  35. On the Relationship Between Story Points and Development Effort in Agile Open-Source Software
  36. MEG: Multi-objective Ensemble Generation for Software Defect Prediction
  37. On the use of evaluation measures for defect prediction studies
  38. Py2Cy
  39. A versatile dataset of agile open source software projects
  40. Green AI
  41. Privileged and unprivileged groups
  42. Did You Do Your Homework? Raising Awareness on Software Fairness and Discrimination
  43. Diversifying Focused Testing for Unit Testing
  44. Fairea: a model behaviour mutation approach to benchmarking bias mitigation methods
  45. The effect of offspring population size on NSGA-II
  46. Enhancing Genetic Improvement of Software with Regression Test Selection
  47. Artifact for Enhancing Genetic Improvement of Software with Regression Test Selection
  48. FrUITeR: a framework for evaluating UI test reuse
  49. A new approach to distribute MOEA pareto front computation
  50. Optimising word embeddings with search-based approaches
  51. The importance of accounting for real-world labelling when predicting software vulnerabilities
  52. Some challenges for software testing research (invited talk paper)
  53. Comparing the effectiveness of three parallelisation approaches for genetic algorithms
  54. Linear Programming as a Baseline for Software Effort Estimation
  55. Multi-objective software effort estimation
  56. A parallel genetic algorithms framework based on Hadoop MapReduce
  57. Exploiting prior-phase effort data to estimate the effort for the subsequent phases
  58. The plastic surgery hypothesis