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生产(或现场)功能故障，这些故障会导致现场故障，这会带来昂贵的后果，而且修复成本也很高。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错误存储库的研究表明，程序员平均花费近50%的时间来定位生产错误，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开发了一种将概率图形模型应用于软件工程问题的综合教学方法。此建议的目标是创建一种新的理论基础，允许利益相关者仅使用症状(例如，输出值的符号不正确)，而无需利用已部署的应用程序来收集运行时数据，而无需使用Oracle进行任何测试来发现故障，无需执行成功和失败的对比运行，也无需从现场故障收集运行时数据，即可高度精确地自动预测和定位现场故障的功能故障。有了这个理论基础，研究人员可以通过将基于概率图形模型的结果作为共同抽象来扩展彼此的结果，从而更紧密地合作规划未来的故障定位。仅根据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的故障位置，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)自动获得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)基于模型的差异诊断用于执行溯因推理的新方法，以根据现场故障的症状定位生产故障；以及(3)用于评估定位生产故障的算法有效性的综合实验框架。除了本地化生产功能故障之外，该实现还可以用作广泛的实验平台，用于为各种软件调试和测试想法创建和测试假设，例如用于指导测试选择和优先级排序。