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.

与现场故障相比，部署软件的行为不正确，很少有问题对人的负面影响更大。就像人类解剖学不同的解剖学会阻止医疗专业人员使用症状快速诊断疾病一样，生产故障本地化也需要软件专业人员的巨大努力，因为每个软件应用程序都有自己的独特结构，而且程序员必须花费大量时间来理解它，即使在较小的应用程序上也是如此。现场失败不仅会使客户对软件应用程序的信心，而且由于软件应用程序支持我们生活的各个方面，因此它们的成本（有时是人类的成本）。尽管有数百种不同的故障定位方法，但尚未解决现场故障的定位生产故障的问题。一个问题是，在部署应用程序时，根据定义不知道生产故障，因此运行现有的测试套件不适用。只有在部署应用程序中发生场失败时，程序员才能开始分析症状以确定导致什么故障。解决时间至关重要，因为应用程序的停机时间通常每分钟成千上万美元。当前，没有解决方案可以在部署的软件应用程序中自动定位功能性生产故障，仅使用现场故障和输入值的症状，而无需部署乐器应用程序，并且没有收集任何运行时数据，并且没有进行任何运行时间的测试，而无需进行成功，而无需进行跑步，并且没有收集现场信息的大量信息，并且没有收集任何运行时的数据。这个I-Corps团队提出了一项新型的研究计划，用于自动定位应用中的功能场现场故障（发音为Al-five），使利益相关者能够输入给定应用程序的失败症状，在给定应用程序的部署以及输入和配置值的部署过程中，并且ALF5可能包含特定的故障和ALF5的位置返回，该代码可能包含特定的故障，并将其推荐给这些故障，并将其推荐给这些故障。失败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，这些计算需要更多时间，可能表明无限循环。该团队计划探索与可以提供有价值系统的潜在客户合作的合作，以展示拟议的创新，并可以帮助团队扩大其创新到商业交付。提出的创新最有可能的市场是：软件系统开发人员，例如IBM全球服务以及智慧和埃森哲；业务流程外包公司像德勤（Deloitte）和CSC这样的公司代表客户托管复杂的应用程序；以及具有复杂生产定制系统的公司，例如保险处理，运输物流。