Mining System Specific Rules from Change Patterns

International audienceA significant percentage of warnings reported by tools to detect coding standard violations are false positives. Thus, there are some works dedicated to provide better rules by mining them from source code history, analyzing bug-fixes or changes between system releases. However, software evolves over time, and during development not only bugs are fixed, but also features are added, and code is refactored. In such cases, changes must be consistently applied in source code to avoid maintenance problems. In this paper, we propose to extract system specific rules by mining systematic changes over source code history, i.e., not just from bug-fixes or system releases, to ensure that changes are consistently applied over source code. We focus on structural changes done to support API modification or evolution with the goal of providing better rules to developers. Also, rules are mined from predefined rule patterns that ensure their quality. In order to assess the precision of such specific rules to detect real violations, we compare them with generic rules provided by tools to detect coding standard violations on four real world systems covering two programming languages. The results show that specific rules are more precise in identifying real violations in source code than generic ones, and thus can complement them

Why and How Java Developers Break APIs

Modern software development depends on APIs to reuse code and increase productivity. As most software systems, these libraries and frameworks also evolve, which may break existing clients. However, the main reasons to introduce breaking changes in APIs are unclear. Therefore, in this paper, we report the results of an almost 4-month long field study with the developers of 400 popular Java libraries and frameworks. We configured an infrastructure to observe all changes in these libraries and to detect breaking changes shortly after their introduction in the code. After identifying breaking changes, we asked the developers to explain the reasons behind their decision to change the APIs. During the study, we identified 59 breaking changes, confirmed by the developers of 19 projects. By analyzing the developers' answers, we report that breaking changes are mostly motivated by the need to implement new features, by the desire to make the APIs simpler and with fewer elements, and to improve maintainability. We conclude by providing suggestions to language designers, tool builders, software engineering researchers and API developers.Comment: Accepted at International Conference on Software Analysis, Evolution and Reengineering, SANER 2018; 11 page

Assessing the Quality of Architectural Design Quality Metrics

As software ages, its quality degrades, leading to pathologies such as architectural decay, a problem that is hard to fight against. First, it is a slow drifting process that results from the natural evolution of software. Second, correcting it is a costly and dangerous operation that has the potential to affect the entire code base. A daring prospect on systems of multi-million lines of code. This spawned a lot of research, for example to measure the quality of a software archi-tectures. Yet a fundamental issue remains, we have very little means of measuring the quality of a given architecture. One of the reasons for this seems to be that building a formal experiment to test these issues is difficult due to the fuzzy nature of what is a good architecture , and to the need to use large and real systems. In this paper we setup an experimental design with well know systems such as Eclipse or JHotDraw that would allow to test the relevance of architectural design metrics. We illustrate it on some well known metrics

Software Metrics for Package Remodularisation

There is a plethora of software metrics \cite{Lore94a, Fent96a, Hend96a, Han00a, Lanz06a} and a large amount of research articles. Still there is a lack for a serious and practically-oriented evaluation of metrics. Often metrics lack the property that the software reengineer or quality expert can easily understand the situation summarized by the metrics. In particular, since the exact notion of coupling and cohesion is complex, a particular focus on such point is important. In the first chapter of the present document, we present a list of software metrics, that are commonly used to measure object-oriented programs. In the second chapter we present our proposition for package metrics that capture package aspects such as information hiding and change impact limits

Why We Engage in FLOSS: Answers from Core Developers

The maintenance and evolution of Free/Libre Open Source Software (FLOSS) projects demand the constant attraction of core developers. In this paper, we report the results of a survey with 52 developers, who recently became core contributors of popular GitHub projects. We reveal their motivations to assume a key role in FLOSS projects (e.g., improving the projects because they are also using it), the project characteristics that most helped in their engagement process (e.g., a friendly community), and the barriers faced by the surveyed core developers (e.g., lack of time of the project leaders). We also compare our results with related studies about others kinds of open source contributors (casual, one-time, and newcomers).Comment: Accepted at CHASE 2018: 11th International Workshop on Cooperative and Human Aspects of Software Engineering (8 pages

APIEvolutionMiner: Keeping API Evolution under Control

International audienceDuring software evolution, source code is constantly refactored. In real-world migrations, many methods in the newer version are not present in the old version (e.g., 60% of the methods in Eclipse 2.0 were not in version 1.0). This requires changes to be consistently applied to reflect the new API and avoid further maintenance problems. In this paper, we propose a tool to extract rules by monitoring API changes applied in source code during system evolution. In this process, changes are mined at revision level in code history. Our tool focuses on mining invocation changes to keep track of how they are evolving. We also provide three case studies in order to evaluate the tool