- Massimiliano Di Penta (Università degli Studi del Sannio, Italy)
- Marco D'Ambros (University of Lugano, Switzerland)
- Emitzá Guzmán Ortega
- Amir Molzam Sharifloo
- Dávid Tengeri
- Melinda Tóth
- Zuoning Yin
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In past and recent years many researchers have developed bug prediction models, i.e., models aimed at identifying source code artifacts that will likely contain a fault. The aim of such models would be to recommend to developers which artifacts would require a better Verification and Validation, because they would likely exhibit faults in the near future.
Some of these models predict fault-proneness using a pool of metrics extracted from a software release , e.g., the Chidamber & Kemerer  metrics suite. Others models are, instead, based on information extracted from source code changes  or previous defects .
Researchers also proposed models that take into account the effort necessary to inspect an artifact during defect prediction (e.g. with code review) . The intent of effort-aware defect prediction is not to predict if an artifact is bug-prone or not, but rather to output a set of artifacts for which the ratio of effort spent for number of defects found is maximized.
Despite the numerous research efforts in this field, the adoption of fault prediction models is, in practice, still fairly limited.
The goal of this working group is to investigate (i) the limitations and weaknesses of existing fault prediction models, (ii) how research in this field could favor the adoption of fault-prediction models by practitioners, and (iii) the main open challenges in building fault prediction models.
The study will be conducted by contacting experts of this field-possibly from both academia and industry-during the ESEC-FSE conference and interviewing them to collect information aimed at addressing our goal.
To this purpose, the working group should identify a set of questions to be asked, for example:
1) What do you think are the main threats to validity that affect existing fault prediction models? For example, threats could be due to the quality of the data set , or else could be due to the inter-project (in)applicability of certain models, if not to their lack of generalizability.
2) What kind of fault prediction models are being used among your industrial partners/in your company? For example, whether they use very simple models, off-the-shelf tools, or whether someone is actually applying models recently developed by the research community.
3) What do you think are the main barriers for the adoption of fault-prediction models among practitioners? For example, the models are not easy to use, their indications are not easy to interpret, or it is very difficult to tune and apply a model to my projects.
4) In which direction do you think the research community should put effort? For example, to improve models performances, or to improve their generalizability/inter-project applicability, or else to make models more usable and easy to be interpreted by practitioners.
5) What is perceived by researchers as more important for defect prediction: the underlying data (e.g. code metrics, changes, previous defects) or the model (e.g. Random forest, Naive Bayes, SVM)?
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