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对人们的影响更负面，现场故障是指部署的软件行为不正确。就像不同的人体解剖结构会阻止医疗专业人员使用症状快速诊断疾病一样，生产故障定位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自己独特的结构，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，而且还会付出高昂的代价，有时甚至是人命，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，但自动定位现场故障的生产故障的问题尚未解决。一个问题是，在部署应用程序时，根据定义，生产故障是未知的，因此运行现有的测试套件是不适用的。只有当部署的应用程序中发生现场故障时，程序员才可以开始分析症状，以确定是什么故障导致了这些故障。修复时间至关重要，因为应用程序的停机时间通常每分钟花费数千美元。目前，没有解决方案可以仅使用现场故障的症状和输入值以高精度自动地定位部署的软件应用中的功能生产故障，而不部署仪表化应用，不收集任何运行时数据，不具有任何使用预言机的测试，不执行成功和失败的运行，并且不从现场故障收集大量的状态信息。这个I-Corps团队提出了一个新的研究计划，用于自动定位应用程序中功能字段故障的故障（发音为AL-Five），其使得利益相关者能够输入在给定应用程序的部署期间发生的故障的症状以及输入和配置参数值，ALF 5将返回代码中可能包含特定错误的位置，并建议对这些位置的代码进行修改以修复这些错误断层故障症状的示例包括但不限于不正确的输出值、程序崩溃以及花费比它们应该花费的时间多得多的时间的计算，可能指示无限循环。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，以展示拟议的创新，并可以帮助团队将其创新扩展到商业交付。拟议创新最有可能的市场是：软件系统开发商，如IBM全球服务和Sapient和埃森哲;业务流程外包公司，如Deloitte和CSC，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，保险加工、运输物流。