CAREER: Improving Software Quality using Dynamically Inferred Models

软件已经成为我们社会不可或缺的一部分，很难想象我们生活的许多方面，包括经济，医疗保健和通信，没有软件。然而，软件很少是完美的，软件缺陷可能会产生严重的后果，例如安全漏洞和私人信息的泄露。虽然这些缺陷的高成本是众所周知的，但软件行业一直无法解决这个问题，因为软件的固有复杂性是如此之高，即使是最好的，最细心的开发人员仍然会犯错误。因此，缺陷不仅常见，而且新缺陷的报告速度通常比开发人员修复它们的速度更快。这使得提高软件质量的问题成为当今社会面临的最关键的挑战之一。这一挑战正是本项目的中心目标。该项目的重点是开发技术和工具，帮助开发人员理解复杂的软件行为和软件更改的行为含义。这些技术和工具旨在通过帮助开发人员更好地完成工作并减少错误来提高软件质量。以这种方式提高软件质量将减少缺陷软件的负面影响，积极影响社会的许多方面，依赖于软件。缺陷和软件质量差的一个重要原因是开发人员认为他们的系统做什么和系统实际做什么之间的不一致。这个项目的重点是通过帮助开发人员可视化，探索和理解他们的系统的运行时行为，以及当开发人员更改代码时行为如何变化来减少这种不一致性。今天，减少这种不一致性的常用方法是直接研究源代码，通过运行时调试器观察执行，以及检测代码中的关键位置并使用日志记录来窥视实现的运行时行为。但是这些过程是高度手动和劳动密集型的，并且经常迫使开发人员一次只考虑单个执行，而不是考虑整个系统的行为。相反，该项目创建了技术和工具，通过从系统执行日志中推断精确，简洁，预测的行为模型来帮助开发人员减少这种不一致性，通过比较，可视化和查询这些模型来帮助开发人员理解和调试任务，并从这些模型中生成测试。这项工作的更广泛的影响是提高软件质量的技术的进步，这反过来又减少了软件缺陷的负面经济和社会影响。

Software has become an integral part of our society and it is hard to imagine many aspects of our lives, including the economy, healthcare, and communication, functioning without software. However, software is rarely perfect and software defects can have serious consequences, such as security breaches and the compromise of private information. While these high costs of defects are well known, the software industry has been unable to remedy the problem because the inherent complexity of software is so high that even the best, most careful developers still make mistakes. As a result, defects are not only common but new defects are typically reported faster than developers can fix them. This makes the problem of improving software quality one of the most critical challenges facing our society today. It is this challenge that is the central goal of this project. The focus of the project is to develop techniques and tools that help developers understand the complex software behavior and the behavioral implications of software changes. These techniques and tools aim to improve the quality of software by helping developers do their jobs better and make fewer mistakes. Improving software quality in such a way will reduce the negative effects of buggy software, positively affecting the many aspects of society that rely on software.One significant cause of defects and poor software quality is the inconsistency between what developers think their system does, and what the system actually does. This project focuses on reducing this inconsistency by helping developers visualize, explore, and understand the runtime behavior of their systems, and how the behavior changes when the developers change the code. Today, common ways to reduce this inconsistency are to study the source code directly, to observe executions via a runtime debugger, and to instrument key locations in the code and use logging to peek into an implementation's runtime behavior. But these processes are highly manual and labor intensive, and often force the developer to think of a single execution at a time, rather than consider the system behavior as a whole. Instead, this project creates techniques and tools that help developers reduce this inconsistency by inferring precise, concise, predictive behavioral models from system execution logs, aiding developers in comprehension and debugging tasks by comparing, visualizing, and querying such models, and generating tests from such models. The broader impact of this work is the advancement of techniques that improve software quality, which, in turn, reduces the negative economic and societal effects of software defects.