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1.
Search-based refactoring for software maintenance   总被引:1,自引:0,他引:1  
The high cost of software maintenance could be reduced by automatically improving the design of object-oriented programs without altering their behaviour. We have constructed a software tool capable of refactoring object-oriented programs to conform more closely to a given design quality model, by formulating the task as a search problem in the space of alternative designs. This novel approach is validated by two case studies, where programs are automatically refactored to increase flexibility, reusability and understandability as defined by a contemporary quality model. Both local and simulated annealing searches were found to be effective in this task.  相似文献
2.
自动程序修复方法研究进展   总被引:1,自引:1,他引:0       下载免费PDF全文
自动程序修复帮助开发者降低人工修复bug的成本.基于测试集的修复方法旨在生成能够通过测试集的代码补丁,以使程序正常运行.回顾了基于测试集的程序修复的现有文献,按照自动修复方法和实证基础两个方面陈述了研究进展.首先,将已有的自动修复方法划分为3类,分别是基于搜索的、基于代码穷举的和基于约束求解的补丁生成方法;其次,细致地描述了程序修复的实证研究基础以及该研究领域中的争议;然后,简要介绍了程序修复的相关技术作为修复方法的补充;最后做出总结,描述了面临的机遇和挑战.  相似文献
3.

Context

Assessing software quality at the early stages of the design and development process is very difficult since most of the software quality characteristics are not directly measurable. Nonetheless, they can be derived from other measurable attributes. For this purpose, software quality prediction models have been extensively used. However, building accurate prediction models is hard due to the lack of data in the domain of software engineering. As a result, the prediction models built on one data set show a significant deterioration of their accuracy when they are used to classify new, unseen data.

Objective

The objective of this paper is to present an approach that optimizes the accuracy of software quality predictive models when used to classify new data.

Method

This paper presents an adaptive approach that takes already built predictive models and adapts them (one at a time) to new data. We use an ant colony optimization algorithm in the adaptation process. The approach is validated on stability of classes in object-oriented software systems and can easily be used for any other software quality characteristic. It can also be easily extended to work with software quality predictive problems involving more than two classification labels.

Results

Results show that our approach out-performs the machine learning algorithm C4.5 as well as random guessing. It also preserves the expressiveness of the models which provide not only the classification label but also guidelines to attain it.

Conclusion

Our approach is an adaptive one that can be seen as taking predictive models that have already been built from common domain data and adapting them to context-specific data. This is suitable for the domain of software quality since the data is very scarce and hence predictive models built from one data set is hard to generalize and reuse on new data.  相似文献
4.
5.
软件缺陷预测通过预先识别出被测项目内的潜在缺陷程序模块,有助于合理分配测试资源,并最终提高被测软件产品的质量。但在搜集缺陷预测数据集的时候,由于考虑了大量与代码复杂度或开发过程相关的度量元,造成数据集内存在维数灾难问题。借助基于搜索的软件工程思想,提出一种新颖的基于搜索的包裹式特征选择框架SBFS。该框架在实现时,首先借助SMOTE方法来缓解数据集内存在的类不平衡问题,随后借助基于遗传算法的特征选择方法,基于训练集选出最优特征子集。在实证研究中,以NASA数据集作为评测对象,以基于前向选择策略的包裹式特征选择方法FW、基于后向选择策略的包裹式特征选择BW、不进行特征选择的Origin作为基准方法。最终实证研究结果表明:SBFS方法在90%的情况下,不差于Origin法。在82.3%的情况下,不差于BW法。在69.3%的情况下,不差于FW法。除此之外,我们发现若基于决策树分类器,则应用SMOTE方法后,可以在71%的情况下,提高模型性能。而基于朴素贝叶斯和Logistic回归分类器,则应用SMOTE方法后,仅可以在47%和43%的情况下,提高模型的预测性能。  相似文献
6.
This paper explores the relationship between software size, development effort and team size. We propose an approach aimed at finding the team size where the project effort has its minimum. The approach was applied to the ISBSG repository containing nearly 4000 software projects. Based on the results we provide our recommendation for the optimal or near-optimal team size in seven project groups defined by four project properties.  相似文献
7.
Automatic software testing tools are still far from ideal for real world object-oriented (OO) software. The use of nature inspired search algorithms for this problem has been investigated recently. Testing complex data structures (e.g., containers) is very challenging since testing software with simple states is already hard. Because containers are used in almost every type of software, their reliability is of utmost importance. Hence, this paper focuses on the difficulties of testing container classes with nature inspired search algorithms. We will first describe how input data can be automatically generated for testing Java containers. Input space reductions and a novel testability transformation are presented to aid the search algorithms. Different search algorithms are then considered and studied in order to understand when and why a search algorithm is effective for a testing problem. In our experiments, these nature inspired search algorithms seem to give better results than the traditional techniques described in literature. Besides, the problem of minimising the length of the test sequences is also addressed. Finally, some open research questions are given.  相似文献
8.
路红  张莉  岳涛 《软件学报》2016,27(4):901-915
在大规模复杂系统产品线工程中,人工配置难免会导致配置的不一致,即,配置数据会违背预定义的约束(也可以称为一致性约束).对于大规模复杂系统产品线体系结构,比如信息物理系统产品线,往往存在成百上千的可变点以及约束,而且约束与可变点之间存在复杂的依赖关系,为不一致配置的修复带来很大的挑战.为了解决这个问题,针对前期提出的基于多目标搜索以及约束求解技术的自动不一致配置修复推荐框架(Zen-Fix),提出一种改进的IBEA算法(De IBEA).De IBEA通过将差分引入IBEA算法,搜索过程中,基于可行解和不可行解的差分变异产生后代,最终为用户推荐符合预定义约束并且对于配置效率来说最优的配置修复方案.基于一个工业案例海底油田采控系统产品线为例,通过模拟一个产品的配置过程,产生了10 189个优化问题,结果表明:Zen-Fix框架结合De IBEA算法,可以实时地为用户提供较优的不一致配置修复方案.此外,通过对这10 189个问题的推荐方案进行对比,证明了De IBEA算法无论从时间效率还是搜索性能上都优于原始的IBEA算法.  相似文献
9.
江磊  许畅  陈小康 《计算机科学》2014,41(11):40-45
近年来,随着智能设备的普及和传感技术的发展,上下文感知程序的应用越来越广泛。但是由于环境噪声难以预测和控制,程序所获得的上下文经常存在一致性错误。处理这类错误的方法很多,但大都忽视了两方面的问题:1)不同一致性约束之间存在相互干扰;2)处理这类错误的操作本身可能对程序的正常运行造成负面影响。以处理这两方面的问题为目标,提出了一种新的基于搜索的上下文一致性错误处理方法,亦即既设计出一个搜索空间来查找避免约束间相互干扰和对程序产生负面影响的解,又采用了一种增量式评估方案来加速搜索的效率。经实验评估,新方法能够在很短的时间内达到非常接近最优解的效果。  相似文献
10.
王赞  樊向宇  邹雨果  陈翔 《软件学报》2016,27(4):879-900
基于程序频谱的缺陷定位方法可以有效地辅助开发人员定位软件内部缺陷,但大部分已有自动化方法在解决多缺陷定位问题时表现不佳,部分效果尚可的方法因复杂度较高或需要开发人员较多交互而仍需进一步改善.为改善上述问题,提出一种基于遗传算法的多缺陷定位方法 GAMFal,具体来说:首先基于搜索的软件工程思想对多缺陷定位问题进行建模,构建了候选缺陷分布的染色体编码方式,并基于扩展的Ochiai系数计算个体的适应度值;随后使用遗传算法在解空间中搜索具有最高适应度值的候选缺陷分布,在终止条件被满足后返回最优解种群;最后根据这个种群对程序实体进行排序.这样开发人员可以依次对程序实体进行检查并最终确定多个缺陷的具体位置.实证研究以Siemens套件中的7个程序和Linux的3个程序(gzip、grep和sed)作为评测对象,并扩展传统的定位方法评测标准EXAM至EXAMF和EXAML,通过与其他经典的缺陷定位方法(Tarantula、Improved Tarantula及Ochiai)进行对比,并通过Friedman检测和最小显著性差异测试可得,提出的GAMFal方法在整体定位效率方面优于传统方法,且需要更少的人工交互.除此之外,GAMFal的执行时间也在可接受的范围之内.  相似文献
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