SHF: Small: Automatically Localizing Functional Faults In Deployed Software Applications

SHF：小型：自动定位已部署软件应用程序中的功能故障

• 批准号: 1615563
• 负责人: Mark Grechanik
• 金额: $ 35.09万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1615563&HistoricalAwards=false
• 关键词: SHF; Small; Automatically; Localizing; Functional

Even though most software applications are tested before they are released to customers, these applications still contain production (or field) functional faults that result in field failures, which have costly consequences and are expensive to fix. Due to their limitations, existing automatic debugging approaches do not adequately isolate and identify production faults for field failures. Prior interviews of test managers and studies of bug repositories revealed that programmers spent close to 50% of their time on average to localize production faults, which is a major factor in software system and software project failures. The educational innovation of this project is in developing an integrated approach to teaching by applying probabilistic graphical models to software engineering problems. The goal of this proposal is to create a novel theoretical foundation that allows stakeholders to predict and localize functional faults for field failures automatically with a high degree of precision using symptoms only (e.g., the sign of the output value is incorrect) and without instrumenting deployed applications to collect runtime data, thus avoiding the deployment runtime overhead, and without having any tests with oracles to uncover the fault, without performing contrasting successful and failed runs, and without collecting runtime data from field failures. With this theoretical foundation, researchers can collaborate more closely in planning the future of fault localization by expanding each other's results based on probabilistic graphical models as common abstractions. Based only on failure symptoms occurring during deployment of a given application, the location of faults in the source code will be determined, as well as navigation paths from likely faults to the code that can fix these faults. The project will create, evaluate and deploy: (1) new theories, algorithms and techniques for automatically obtaining probabilistic graphical models that approximate specific fault models for software applications; (2) a novel way in which model-based differential diagnoses are used to perform abductive reasoning to localize production faults given symptoms for field failures, and (3) a comprehensive experimentation framework for evaluating the effectiveness of the algorithms for localizing production faults. In addition to localizing production functional faults, the implementation can be used as a broad experimental platform for creating and testing hypotheses for various software debugging and testing ideas, e.g., for guiding test selection and prioritization.

尽管大多数软件应用程序在发布给客户之前都经过了测试，但这些应用程序仍然包含导致现场故障的生产（或现场）功能错误，这些错误会产生昂贵的后果，并且修复起来也很昂贵。由于其局限性，现有的自动调试方法不能充分隔离和识别现场故障的生产故障。之前对测试经理的采访和对bug存储库的研究表明，程序员平均花费近50%的时间来定位产品错误，这是软件系统和软件项目失败的主要因素。这个项目的教育创新之处在于通过将概率图形模型应用于软件工程问题来开发一种综合的教学方法。该提案的目标是创建一个新颖的理论基础，允许利益相关者仅使用症状（例如，输出值的符号不正确）就可以高度精确地自动预测和定位现场故障的功能故障，而无需检测已部署的应用程序来收集运行时数据，从而避免部署运行时开销，并且无需使用oracle进行任何测试来发现故障。不执行成功和失败运行的对比，也不从字段故障中收集运行时数据。有了这个理论基础，研究人员可以更紧密地合作，在规划故障定位的未来，通过扩展彼此的结果，基于概率图模型作为共同的抽象。仅根据在给定应用程序部署期间出现的故障症状，就可以确定源代码中故障的位置，以及从可能的故障到可以修复这些故障的代码的导航路径。该项目将创建、评估和部署：(1)用于自动获取近似软件应用程序特定故障模型的概率图形模型的新理论、算法和技术；(2)一种基于模型的鉴别诊断的新方法，在给定现场故障症状的情况下进行溯因推理，以定位生产故障；(3)一个综合的实验框架，用于评估定位生产故障算法的有效性。除了本地化生产功能故障之外，该实现还可以作为一个广泛的实验平台，用于为各种软件调试和测试思想创建和测试假设，例如指导测试选择和优先级。