Leveraging Software Analytics to Support Software Maintenance and Evolution

利用软件分析支持软件维护和发展

• 批准号: RGPIN-2016-04712
• 负责人: Guerrouj, Latifa
• 金额: $ 1.97万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=652609
• 关键词: Leveraging; Software; Analytics; Support; Maintenance

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.

今天，软件开发项目产生了大量的数据；包括源代码、开发人员的讨论、功能规范、错误报告、执行跟踪以及最终用户反馈等。数据在现代软件开发中扮演着重要的角色，因为它包含关于软件和服务质量以及软件项目动态的重要知识。在源代码中，开发人员使用应用程序编程接口(API)作为代码重用的一种手段。API允许开发人员通过为库和框架提供高级功能并封装实现细节来与其交互。目的是降低开发成本，提高系统质量。然而，最近的研究表明，API经常涉及挑战，这些挑战主要与其设计、质量以及学习资源(如高质量、完整和正确的文档或代码示例的可用性)有关。此外，API发展很快。如此快速的发展使得开发人员很难随时关注API的变化。显然，需要适当和完整的API文档来帮助开发人员在他们的软件维护和发展任务中，从而提高他们的生产力。该研究计划的长期目标是通过利用软件分析(即使用数据及其分析进行决策)来帮助增强和重新记录传统的API文档。这一目标将通过为软件组织提供可操作、准确和高效的情景感知API摘要方法和推荐系统来实现，与过去的研究不同，这些方法和推荐系统不仅将描述API元素的目的及其用法，而且还将描述其重大变化(以及在可能的情况下其基本原理)、与其他元素的依赖关系以及其质量。我们的研究方法将得到广泛认可的技术的支持，这些技术用于挖掘开发历史、跟踪、非正式文档(例如，电子邮件痕迹、错误报告和代码审查)，以及高效的数据挖掘算法，以发现API元素之间的关系，同时保持其更改/使用的顺序。这项研究产生的方法、系统和工具将通过涉及开放源码系统和(可能)工业系统的大规模经验研究进行评估，而其有用性将通过与专业开发人员和项目经理进行用户研究的方式进行评估。