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- How developers engage with static analysis tools in different contexts
- Improving change prediction models with code smell-related information
- MALTESQUE 2019 Workshop Summary
- On the performance of method-level bug prediction: A negative result
- Discovering community patterns in open-source: a systematic approach and its evaluation
- Fine-grained just-in-time defect prediction
- Healthcare Android apps: a tale of the customers’ perspective
- How the experience of development teams relates to assertion density of test classes
- Not all bugs are the same: Understanding, characterizing, and classifying bug types
- On the impact of code smells on the energy consumption of mobile applications
- Test-driven code review: an empirical study
- A graph-based dataset of commit history of real-world android apps
- Automatic test smell detection using information retrieval techniques
- Beyond technical aspects: How do community smells influence the intensity of code smells?
- Context is king: The developer perspective on the usage of static analysis tools
- Enhancing change prediction models using developer-related factors
- How is video game development different from software development in open source?
- Information needs in contemporary code review
- Mining file histories: Should we consider branches?
- On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation
- On the relation of test smells to software code quality
- Poster: How Do Community Smells Influence Code Smells?
- Re-evaluating method-level bug prediction
- Self-reported activities of android developers
- An exploratory study on the relationship between changes and refactoring
- Developer-related factors in change prediction: an empirical assessment
- Lightweight detection of android-specific code smells: The adoctor project
- Petra: a software-based tool for estimating the energy profile of android applications
- Software-based energy profiling of android apps: Simple, efficient and reliable?
- The scent of a smell: An extensive comparison between textual and structural smells
- Toward a smell-aware bug prediction model
- A textual-based technique for smell detection
- Automatic test case generation: What if test code quality matters?