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.

即使大多数软件应用程序在将其发布给客户之前进行了测试，但这些应用程序仍包含导致现场故障的生产（或现场）功能故障，这些故障具有昂贵的后果且修复费用昂贵。由于其局限性，现有的自动调试方法不能充分隔离并确定现场故障的生产故障。 先前对测试经理的访谈和对错误存储库的研究表明，程序员平均花费了近50％的时间来定位生产故障，这是软件系统和软件项目故障的主要因素。该项目的教育创新是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，使利益相关者能够预测和定位功能故障，以自动使用症状自动使用症状（例如，输出价值的迹象是不正确的），并且在不避免避免执行的运行时间的情况下，并且不得避免执行任何操作，并且不避免执行运行时间，并且不得不避免执行任何操作，并且不得不避免进行操作。运行，而无需从字段失败收集运行时数据。有了这个理论基础，研究人员可以通过基于概率图形模型作为常见的摘要来扩大彼此的结果，从而更加紧密地计划错误本地化的未来。 仅基于在给定应用程序部署期间发生的故障症状，将确定源代码中故障的位置，以及从可能的故障到可以解决这些故障的代码的导航路径。该项目将创建，评估和部署：（1）用于自动获取概率图形模型的新理论，算法和技术，该模型近似于软件应用程序的特定故障模型； （2）一种新型的方式，其中使用基于模型的差异诊断来执行绑架性推理，以定位生产故障，并且给定野外故障的症状，以及（3）一个全面的实验框架，用于评估算法对定位生产故障的有效性。除了本地化生产功能故障外，该实现还可以用作广泛的实验平台，用于创建和测试各种软件调试和测试想法的假设，例如指导测试选择和优先级。