软件可测试性度量及其趋势的定性和全面分析

Siddhi Purohit, Simran Singh, Mansi Agarwal, Neha Verma
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引用次数: 0

摘要

技术的进步导致了关键和复杂软件的诞生。这些都需要彻底的测试,以确保生产可靠和高性能的软件。测试是软件生命周期中最昂贵的部分,对测试工作的估计可以导致对资源的明智利用。可测试性是在软件中发现故障的便利性,它的评估可以降低成本并延长软件的使用寿命。然而,该领域缺乏足够的研究和标准化。目前的研究主要集中在面向对象范式和代码级可测试性方面。本文旨在对可测试性的度量、模型以及程序属性与可测试性之间的关系进行综述。通过本次调查,选取了29项研究进行分析。我们的研究得出结论,代码级别的可测试性度量和与尺寸相对应的设计度量是常用的。这些指标与测试成果之间的关系是使用各种机器学习模型建立的,并呈现出具有各种程序属性的可测试性趋势。这种全面的回顾有助于确定合适的度量、具有各种程序属性的预期趋势,以及选择合适的模型来自动化过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Qualitative and Comprehensive Analysis of Software Testability Metrics and their Trends
Advancement in technology has resulted in birth of critical and complex software. These require thorough testing to ensure production of reliable and high performance software. Testing is the most expensive part of the software life cycle and an estimate of testing efforts can result in smart utilization of resources. Testability is the ease of finding faults in a software and its estimate can reduce costs and increase life of the software. However the area lacks adequate research and standardisation. Current studies majorly report on Object Oriented paradigm and code level testability. This study aims to provide a broader review on Testability metrics, models and establishing relationship between program attributes and testability. Through this survey 29 studies have been selected for analysis. Our studies conclude that testability metrics at code level and design metric corresponding to size are commonly used. Relationships amongst these metrics with their test efforts are established using various machine learning models and presents testability trends with various program attributes. This comprehensive review helps in identifying suitable metric, expected trends with various program attributes and selection of suitable models to automate processes.
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