I-Corps: Automatically Localizing Functional Faults In Deployed Software Applications

I-Corps：自动定位已部署软件应用程序中的功能故障

• 批准号: 1547597
• 负责人: Mark Grechanik
• 金额: $ 5万
• 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: 2015
• 资助国家: 美国
• 起止时间: 2015-07-15 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1547597&HistoricalAwards=false
• 关键词: Corps; Automatically; Localizing; Functional; Faults

Very few problems impact people more negatively than field failures, where deployed software behaves incorrectly. Just like distinct human anatomies would prevent medical professionals from quickly diagnosing diseases using symptoms, production fault localization requires a huge effort from software professionals, since each software application has its own unique structure and programmers must spend a lot of time to understand it even for smaller applications. Not only do field failures zap customer confidence in software applications, but also they cost dearly, sometimes in human lives, since software applications support all aspects of our lives. Despite hundreds of different approaches for fault localization, the problem of localizing production faults for field failures automatically is unsolved. A problem is that production faults are not known by definition when the application is deployed, therefore running existing test suites is not applicable. Only when field failures occur in a deployed application can programmers start analyzing the symptoms to determine what faults cause them. Time to fix is critical, since the applications' downtime often costs thousands of dollars per minute. Currently, there is no solution that can automatically localize functional production faults in deployed software applications with a high degree of precision using only symptoms of the field failures and input values and without deploying instrumented applications and without collecting any runtime data and without having any tests with oracles, without performing successful and failed runs, and without collecting large amounts of state information from field failures. This I-Corps team proposes a novel research program for Automatically Localizing Faults For Functional Field Failures in Applications (pronounced as al-five) that enables stakeholders to enter symptoms of a failure that occurs during deployment of a given application and the input and configuration parameter values, and ALF5 will return locations in the code that are likely to contain specific faults and it recommends modifications to the code at these locations that can fix these faults. Examples of symptoms of failures include but not limited to incorrect output values, program crashes and computations that take much more time that they are supposed to, possibly indicating infinite loops. The team plans to explore partnering with potential customers who can provide production worthy systems upon which to demonstrate the proposed innovation and can help the team scale up its innovation to commercial delivery. The most likely markets for the proposed innovation are: software systems developers, like IBM Global Services and Sapient and Accenture; business process outsourcing firms like Deloitte and CSC, that host complex applications on behalf of customers; and companies with complex in-production custom systems, e.g., insurance processing, transportation logistics.

很少有问题比现场故障（部署的软件行为不正确）对人们的影响更大。就像不同的人体解剖学会阻止医疗专业人员通过症状快速诊断疾病一样，生产故障定位也需要软件专业人员付出巨大的努力，因为每个软件应用程序都有其独特的结构，程序员必须花费大量时间来理解它，即使是较小的软件应用程序。应用程序。现场故障不仅会削弱客户对软件应用程序的信心，而且还会造成高昂的代价，有时甚至会造成人员伤亡，因为软件应用程序支持我们生活的各个方面。尽管有数百种不同的故障定位方法，但针对现场故障自动定位生产故障的问题尚未解决。问题是，在部署应用程序时，生产故障的定义是未知的，因此运行现有的测试套件不适用。只有当已部署的应用程序中发生现场故障时，程序员才能开始分析症状以确定导致故障的原因。修复时间至关重要，因为应用程序的停机时间通常每分钟损失数千美元。目前，还没有一种解决方案可以仅使用现场故障症状和输入值，在不部署仪表化应用程序、不收集任何运行时数据、不进行任何测试的情况下，高精度地自动定位已部署软件应用程序中的功能性生产故障。预言机，无需执行成功和失败的运行，也无需从现场故障中收集大量状态信息。该 I-Corps 团队提出了一项新颖的研究计划，用于自动定位应用程序中功能性现场故障的故障（发音为 al-5），该计划使利益相关者能够输入给定应用程序部署期间发生的故障症状以及输入和配置参数值，并且 ALF5 将返回代码中可能包含特定错误的位置，并建议修改这些位置的代码以修复这些错误。故障症状的示例包括但不限于不正确的输出值、程序崩溃和计算花费的时间比预期多得多，可能表明无限循环。该团队计划探索与潜在客户合作，这些客户可以提供具有生产价值的系统来展示所提出的创新，并可以帮助团队将其创新扩大到商业交付。所提议的创新最有可能的市场是： 软件系统开发商，如 IBM 全球服务部、Sapient 和 Accenture；德勤 (Deloitte) 和 CSC 等业务流程外包公司代表客户托管复杂的应用程序；以及拥有复杂的生产定制系统的公司，例如保险处理、运输物流。