Automatic Static Analysis Tools (ASATs) are widely used by software developers to diffuse and enforce coding practices. Yet, we know little about the documentation of ASATs, despite it being critical to learn about the coding practices in the first place. We shed light on this through several contributions. First, we analyze the documentation of more than 100 rules of 16 ASATs for multiple programming languages, and distill a taxonomy of the purposes of the documentation---What triggers a rule; Why it is important; and how to Fix an issue---and its types of contents. Then, we conduct a survey to assess the effectiveness of the documentation in terms of its goals and types of content. We highlight opportunities for improvement in ASAT documentation. In particular, we find that the Why purpose is missing in half of the rules we survey; moreover, when the Why is present, it is more likely to have quality issues than the What and the Fix.

Coding practices are increasingly used by software companies. Their use promotes consistency, readability, and maintainability, which contribute to software quality. Coding practices were initially enforced by general-purpose linters, but companies now tend to design and adopt their own company-specific practices. However, these company-specific practices are often not automated, making it challenging to ensure they are shared and used by developers. Converting these practices into linter rules is a complex task that requires extensive static analysis and language engineering expertise.In this paper, we seek to answer the following question: can coding practices be learned automatically from examples manually tagged by developers? We conduct a feasibility study using CodeBERT, a state-of-the-art machine learning approach, to learn linter rules. Our results show that, although the resulting classifiers reach high precision and recall scores when evaluated on balanced synthetic datasets, their application on real-world, unbalanced codebases, while maintaining excellent recall, suffers from a severe drop in precision that hinders their usability.

Context: Expert identification is becoming critical to ease the communication between developers in case of global software development or to better know members of large software communities. To quickly identify who are the experts that will best perform a given development task, both the assignment of skills to developers and the computation of their corresponding expertise level have to be automated. Since the real level of expertise is tedious to assess, our challenge is to identify developers having a significant level of experience with respect to a skill. Method: In this paper we propose XTic, an approach that takes up this challenge with the intent to be accurate and efficient. XTic provides a language to specify skills. It also provides an automatic process that extracts skills and experience levels from source code repositories. Our approach is based on the idea that an expert has a high level of experience with respect to a skill. Results: We have validated XTic both on open source and industrial projects to measure its accuracy and its efficiency. The results we obtained show that its accuracy is between moderate and strong and that it scales well with medium and large size software projects. Conclusion: XTic supports the specification of a diversity of developer skills and the extraction of the expertise of these developers under the form of level of experience.

ABSTRACT Software intensively depends on external libraries whose relevance may change during its life cycle. As a consequence, software developers must periodically reconsider the libraries they depend on, and must think about replacing them for more relevant ones. We refer to this practice as library migration. To find the best replacement for their library, they can rely on information over the Web, but they get quickly overwhelmed by the amount of data they gather. Making the right choice in this context constitutes the topic of our work. The solution we propose is to exhibit and mine the library migrations trends computed by performing a study of a large set of software projects. To perform this analysis, we have defined an automatic approach to compute library dependencies and a semi‐automatic approach that identifies library migrations. Then, we propose a deep analysis of the library migration phenomena by performing a descriptive study of a large set of software projects stored on the Githubplatform. Second, based on our descriptive study, we propose a support to developers who want to migrate their libraries. The main result of our study is that recommendations of libraries can be inferred from the analysis of the migration trends. Copyright © 2014 John Wiley & Sons, Ltd.

SUMMARY Ensuring models’ consistency is a key concern when using a model‐based development approach. Therefore, model inconsistency detection has received significant attention over the last years. To be useful, inconsistency detection has to be sound, efficient, and scalable. Incremental detection is one way to achieve efficiency in the presence of large models. In most of the existing approaches, incrementalization is carried out at the expense of the memory consumption that becomes proportional to the model size and the number of consistency rules. In this paper, we propose a new incremental inconsistency detection approach that only consumes a small and model size‐independent amount of memory. It will therefore scale better to projects using large models and many consistency rules. Copyright © 2012 John Wiley & Sons, Ltd.