
Topic
- Computing and Processing
- Components, Circuits, Devices and Systems
- Communication, Networking and Broadcast Technologies
- Power, Energy and Industry Applications
- Signal Processing and Analysis
- Robotics and Control Systems
- General Topics for Engineers
- Fields, Waves and Electromagnetics
- Engineered Materials, Dielectrics and Plasmas
- Bioengineering
- Transportation
- Photonics and Electrooptics
- Engineering Profession
- Aerospace
- Geoscience
- Nuclear Engineering
- Career Development
- Emerging Technologies
- Telecommunications
- English for Technical Professionals
David Bowes
Also published under:D. Bowes
Affiliation
School of Computing and Communications, Lancaster University, Lancaster, U.K.
Topic
Automated Program Repair,Bug Fixes,Fault Location,Human Studies,Professional Development,Refactoring,Software Development,Software Engineering,Software Repositories,Thematic Analysis,Use Of Tools,Ability Of Tools,Adjusted Rand Index,Automatic Tool,Benchmark Suite,Benefit Of This Approach,Centroid,Clone Detection,Clustering Algorithm,Code Changes,Code Modifications,Coded Based,Cohen’s Kappa,Continuous Line,Debugging,Defect Prediction,Degree Of Scepticism,Development Experience,Development Process,Frustration,General Attitudes,General Feeling,General Kind,General Working,Highest Qualification,Human Factor,Item Clusters,Kendall's Coefficient,Key Search Terms,List Of Criteria,Lot Of Time,Negotiated Agreement,Number Of Papers,Open Text Responses,Open-source Projects,Open-source System,Pairs Of Reviewers,Pareto Principle,Patch Features,Performance Of Tools,
Biography
David Bowes is a senior lecturer in computer science at Lancaster University, Lancaster, LA1 4YW, U.K. Bowes received his Ph.D. in software engineering from the University of Hertfordshire, Hatfield, U.K. He is an expert in software development and brings a focus on the production of successful tools. He has previously developed tools to collect data, analyze defective code, and assess the performance of defect prediction models. He has a deep knowledge of analysis methods, having built many defect prediction models. Contact him at [email protected].