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Improving Fault Detection in Modified Code—A Study from the Telecommunication Industry

机译:用修改后的代码改进故障检测—来自电信行业的研究

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摘要

Many software systems are developed in a number of consecutive releases. In each release not only new code is added but also existing code is often modified. In this study we show that the modified code can be an important source of faults. Faults are widely recognized as one of the major cost drivers in software projects. Therefore, we look for methods that improve the fault detection in the modified code. We propose and evaluate a number of prediction models that increase the efficiency of fault detection. To build and evaluate our models we use data collected from two large telecommunication systems produced by Ericsson. We evaluate the performance of our models by applying them both to a different release of the system than the one they are built on and to a different system. The performance of our models is compared to the performance of the theoretical best model, a simple model based on size, as well as to analyzing the code in a random order (not using any model). We find that the use of our models provides a significant improvement over not using any model at all and over using a simple model based on the class size. The gain offered by our models corresponds to 38~57% of the theoretical maximum gain.
机译:许多软件系统是在许多连续发行版中开发的。在每个版本中,不仅添加了新代码,而且经常修改现有代码。在这项研究中,我们表明修改后的代码可能是错误的重要来源。故障已被广泛认为是软件项目中主要的成本驱动因素之一。因此,我们在修改后的代码中寻找改善故障检测的方法。我们提出并评估了许多可提高故障检测效率的预测模型。为了建立和评估我们的模型,我们使用从爱立信生产的两个大型电信系统中收集的数据。我们通过将模型应用到与构建模型不同的系统版本和不同系统上,来评估模型的性能。我们将模型的性能与理论上最好的模型(基于大小的简单模型)的性能进行比较,并以随机顺序分析代码(不使用任何模型)。我们发现,与完全不使用任何模型以及使用基于类大小的简单模型相比,使用模型提供了显着的改进。我们模型提供的增益相当于理论最大增益的38〜57%。

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