Leveraging Software Analytics to Support Software Maintenance and Evolution

点击翻译按钮获取中文摘要

Software development projects generate impressive amounts of data today; including source code, developers' discussions, feature specifications, bug reports, execution traces, as well as end-users feedback, etc. Data plays an essential role in modern software development because it contains a significant knowledge about the quality of software and services as well as the dynamics of software projects. In source code, developers make use of Application Programming Interfaces (API) as a mean of code reuse. APIs allow developers to interact with libraries and frameworks by providing them with high-level features and encapsulating implementation details. The aim is to reduce the development cost and increase the system's quality. Recent research has shown that APIs, however, often involve challenges which are mainly related to their design, quality, as well as learning resources such as the availability of high-quality, complete, and correct documentation or code examples. In addition, APIs evolve fast. Such rapid evolution makes it difficult for developers to stay tuned with changes to the APIs. Clearly, there is a need for proper and complete API documentation that can help developers during their software maintenance and evolution tasks, and consequently increase their productivity. The long-term goal of this research program is to help enhance and redocument traditional API documentation, by leveraging software analytics, that is the use of data, and their analysis for making decisions. This goal will be achieved by providing software organizations with actionable, accurate, and efficient context-aware API summarization approaches and recommendation systems, that unlike past research, will describe not only the purpose of an API element and its usage, but also its significant changes (and whenever possible their rationale), dependencies with other elements, and its quality. Our research methodology will be supported by widely-acknowledged techniques for mining development history, traces, informal documentation (e.g., email treads, bug reports, and code reviews), as well as efficient data mining algorithms to discover relations between API elements, while preserving the order of their changes/uses. The approaches, systems, and tools resulting from this research will be evaluated through large-scale empirical studies involving open-source systems and (possibly) industrial ones, while their usefulness will be assessed by means of user studies with professional developers and project managers.