SHF: Small: Science and Tools for Intelligent Developer Testing

SHF：小型：智能开发人员测试的科学和工具

基本信息

批准号：
1816615
负责人：
Tianyin Xu
金额：
$ 50万
依托单位：
University of Illinois at Urbana-Champaign
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816615&HistoricalAwards=false
关键词：
SHF Small Science Tools Intelligent

项目摘要

Software dependability plays a critical role in United States businesses, government, and society. Although much progress has been made in software verification and validation, software testing remains by far the most widely used technique for improving software dependability. Among various types of testing, developer testing has been widely recognized as an important and valuable means of improving software dependability. The popularity and benefits of developer testing have been well witnessed in industry; however, manual developer testing is known to be labor intensive, and is often insufficient in comprehensively exercising behavior of the software under test to expose its hidden bugs. To address the issue, one of the common ways is to use testing infrastructures and tools to reduce or complement manual testing effort to achieve higher software dependability. In the past decade, the software testing research community has made significant progress in automatic test generation. With various recent scientific advances by the research community, a question naturally arises: what would be the audacious goal for the field of developer testing in the upcoming decade to bring a much higher testing effectiveness and efficiency to developers? To address this question, this project investigates the science and tools of intelligent developer testing, fundamentally advancing knowledge and understanding in foundations, techniques, and tools for intelligent developer testing. The project improves software dependability by revealing more bugs during software development before they manifest in deployed software.The project focuses on science and tools for instilling intelligence from two major ways (natural language interfacing and continuous learning) into developer testing tools as part of the efforts for realizing the vision of intelligent software engineering. The project develops novel and practical techniques and tools of intelligent developer testing that have high potential to impact the industry. In particular, the project focuses on parameterized unit tests, which are an improvement over conventional unit tests because they can easily be extended by automated tools to increase code coverage while reusing developer-written oracles. The PI plans to develop intelligent tools that will make it significantly easier for developers to write parameterized unit tests with the goal of automatically translating developer intents into parts of executable test cases. This project advances the science by exploring a series of questions, e.g., how to define or determine levels of intelligence in developer testing, how to bring high levels of intelligence in developer testing tools, how to synergistically integrate machine intelligence and human intelligence (e.g., domain knowledge or insight) to effectively tackle challenging tasks in developer testing. The project involves research collaborations with industrial partners and involves participation of students from underrepresented groups.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

软件可靠性在美国企业、政府和社会中发挥着至关重要的作用。尽管在软件验证和确认方面已经取得了很大进展，但软件测试仍然是迄今为止使用最广泛的提高软件可靠性的技术。在各种类型的测试中，开发人员测试已被广泛认为是提高软件可靠性的重要且有价值的手段。开发人员测试的受欢迎程度和好处在业界已得到充分证明；然而，众所周知，手动开发人员测试是劳动密集型的，并且通常不足以全面地测试被测软件的行为以暴露其隐藏的错误。为了解决这个问题，常见的方法之一是使用测试基础设施和工具来减少或补充手动测试工作，以实现更高的软件可靠性。在过去的十年中，软件测试研究社区在自动测试生成方面取得了重大进展。随着研究界最近取得的各种科学进展，一个问题自然而然地出现了：在未来十年中，开发人员测试领域的大胆目标是什么，以便为开发人员带来更高的测试有效性和效率？为了解决这个问题，该项目研究了智能开发人员测试的科学和工具，从根本上增进了对智能开发人员测试的基础、技术和工具的知识和理解。该项目通过在软件开发过程中在已部署的软件中显现之前揭示更多错误来提高软件可靠性。该项目侧重于将智能从两种主要方式（自然语言接口和持续学习）灌输到开发人员测试工具中的科学和工具，作为努力的一部分实现智能软件工程的愿景。该项目开发了智能开发人员测试的新颖实用的技术和工具，这些技术和工具对行业具有很大的影响潜力。特别是，该项目侧重于参数化单元测试，这是对传统单元测试的改进，因为它们可以通过自动化工具轻松扩展，以增加代码覆盖率，同时重用开发人员编写的预言机。 PI 计划开发智能工具，使开发人员能够更轻松地编写参数化单元测试，其目标是将开发人员的意图自动转换为可执行测试用例的一部分。该项目通过探索一系列问题来推动科学发展，例如如何定义或确定开发人员测试中的智能水平、如何在开发人员测试工具中引入高水平的智能、如何协同集成机器智能和人类智能（例如，领域知识或洞察力）来有效地解决开发人员测试中的挑战性任务。该项目涉及与工业合作伙伴的研究合作，并涉及来自代表性不足群体的学生的参与。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（30）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications

SemRegex：一种基于语义的从自然语言规范生成正则表达式的方法

DOI：
10.18653/v1/d18-1189
发表时间：
2018-10
期刊：
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
影响因子：
0
作者：
Zhong, Zexuan;Guo, Jiaqi;Yang, Wei;Peng, Jian;Xie, Tao;Lou, Jian;Liu, Ting;Zhang, Dongmei
通讯作者：
Zhang, Dongmei

Synthesizing contracts correct modulo a test generator

综合合约以测试生成器为模进行校正

DOI：
10.1145/3485481
发表时间：
2021-10-15
期刊：
Proceedings of the ACM on Programming Languages
影响因子：
0
作者：
Angello Astorga;Shambwaditya Saha;Ahmad Dinkins;Felicia Wang;P. Madhusudan;Tao Xie
通讯作者：
Tao Xie

Root Cause Localization for Unreproducible Builds via Causality Analysis Over System Call Tracing

通过系统调用跟踪的因果分析来定位无法重现的构建的根本原因

DOI：
10.1109/ase.2019.00056
发表时间：
2019-11-01
期刊：
2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)
影响因子：
0
作者：
Zhilei Ren;Changlin Liu;Xusheng Xiao;He Jiang;Tao Xie
通讯作者：
Tao Xie

REINAM: reinforcement learning for input-grammar inference

REINAM：用于输入语法推理的强化学习