软件可靠性分析、测试与评估工具——SRATE介绍

软件可靠性分析、测试及评估工具(SRATE)是一个集软件可靠性分析、测试及评估于一体的软件工具。该工具支持多种测试策略下的软件自动化测试及评估试验,包括随机测试策略、自适应测试策略等。该工具还集成了包括欧洲航天局的space软件、浙大CAD&CG国家重点实验室的Intra3D构件库在内的多个典型被测对象。根据工具接口要求可以方便的添加新的测试策略和测试对象。具有自动化程度高、通用性强、配置灵活、易于扩展的特点,是进行软件可靠性分析研究工作的有力工具。     

An empirical study of the effect of complexity, platform, and program type on software development effort of business applications

Several popular cost estimation models like COCOMO and function points use adjustment variables, such as software complexity and platform, to modify original estimates and arrive at final estimates. Using data on 666 programs from 15 software projects, this study empirically tests a research model that studies the influence of three adjustment variables—software complexity, computer platform, and program type (batch or online programs) on software effort. The results confirm that all the three adjustment variables have a significant effect on effort. Further, multiple comparison of means also points to two other results for the data examined. Batch programs involve significantly higher software effort than online programs. Programs rated as complex have significantly higher effort than programs rated as average.     

一种用于软件测试的程序结构划分方法

软件测试中的结构性测试是以程序的结构为基础生成测试用例，以测试准则为判定测试的充分性，由于程序结构的复杂性，难于保证对程序进行充分而高效的测试，本文提出了一种基于程序依赖图的程序结构划分的测试方法，即程序块划分法。该方法难过对程序进行结构划分，将复杂的程序分解为若干程序块，并通过程序块间的数据依赖关系导出各程序块的语义，从而使测试可以在程序块的级别的基础上独立进行。     

A debugging and testing tool for supporting software evolution

This paper describes a tool for debugging programs which develop faults after they have been modified or are ported to other computer systems. The tool enhances the traditional debugging approach by automating the comparison of data structures between two running programs. Using this technique, it is possible to use early versions of a program which are known to operate correctly to generate values for comparison with the new program under development. The tool allows the reference code and the program being developed to execute on different computer systems by using open distributed systems techniques. A data visualisation facility allows the user to view the differences in data structures. By using the data flow of the code, it is possible to locate faulty sections of code rapidly. An evaluation is performed by using three case studies to illustrate the power of the technique.     

测试数据自动生成技术研究

软件测试分为静态分析、路径选择、测试数据生成和动态分析四个阶段,而测试数据的自动生成是软件测试的关键技术之一。文章通过对被测试程序的分析,提出了生成测试数据的平衡力法,对任意的输入变量,判断变量移动范围及进行谓词中变量的函数极小化,得到测试数据,并给出了具体实现方法。     

基于CGOM的软件费用模型研究

软件测试是提高软件质量的重要手段，对测试数据充分的测试准则是软件测试完成的标准．目前，绝大多数的测试准则并不考虑软件费用，容易为了满足测试准则而使用户承担昂贵的测试费用．该文提出了一种随机过程类非齐次泊松过程(NHPP)中的经典模型——G-O模型的改进模型，并基于此模型构造了一个综合了软件设计费用、软件测试费用、软件维护费用、软件失效造成的风险损失的软件费用模型．最后从软件费用出发，讨论了软件的最佳发布时间．     

Using concepts of content‐based image retrieval to implement graphical testing oracles

Automation of testing is an essential requirement to render it viable for software development. Although there are several testing techniques and criteria in many different domains, developing methods to test programs with complex outputs remains an unsolved challenge. This setting includes programs with graphical output, which produce images or interface windows. One possible approach towards automating the testing activity is the use of automatic oracles in which a reference image, taken as correct, can be used to establish a correctness measure in the tested program execution. A method that uses concepts of content‐based image retrieval to facilitate oracle automation in the domain of programs with graphics output is presented. Two case studies, one using a computer‐aided diagnostic system and one using a Web application, are presented, including some reflections and discussions that demonstrate the feasibility of the proposed approach. Copyright © 2011 John Wiley & Sons, Ltd.     

Feature transfer learning by reinforcement learning for detecting software defect

Software defects, produced inevitably in software projects, seriously affect the efficiency of software testing and maintenance. An appealing solution is the software defect prediction (SDP) that has achieved good performance in many software projects. However, the difference between features and the difference of the same feature between training data and test data may degrade defect prediction performance if such differences violate the model's assumption. To address this issue, we propose a SDP method based on feature transfer learning (FTL), which performs a transformation sequence for each feature in order to map the original features to another feature space. Specifically, FTL first uses the reinforcement learning scheme that automatically learns a strategy for transferring the potential feature knowledge from the training data. Then, we use the learned feature knowledge to inspire the transformation of the test data. The classifier is trained by the transformed training data and predicts defects for transformed test data. We evaluate the validity of FTL on 43 projects from PROMISE and NASA MDP using three classifiers, logistic regression, random forest, and Naive Bayes (NB). Experimental results indicate that FTL is better than the original classifiers and has the best performance on the NB classifier. For PROMISE, after using FTL, the average results of F1-score, AUC, MCC are 0.601, 0.757, and 0.350 respectively, which are 24.9%, 2.6%, and 16.7% higher than the original NB classifier results. The number of projects with improved performance accounts for 83.87%, 83.87%, and 64.52%. Similarly, FTL performs well on NASA MDP. Besides, compared with four feature engineering (FE) methods, FTL achieves an excellent improvement on most projects and the average performance is also better than or close to the FE methods.     

A set of complexity metrics for guiding the software test process

Software verification and validation is a domain which is covered by many dynamic test, static analysis, and formal verification techniques. This presents a problem to practitioners with respect to selecting those suitable techniques which can be used successfully. The basic idea of the methodology presented here is to select test techniques which fit the software under test. A dynamic test technique requires that certain program elements are covered, will be sensitive to errors associated with these elements, because executing an error location is a precondition for revealing the error. Furthermore, it is likely that the probability of errors increases with complexity. Complexity can be characterized in terms of several properties which can be used to suggest various testing strategies. The complexity of the various software properties can be measured using appropriate complexity metrics. Properties with unusual high complexity measures should be tested very throughly. The approach described in this paper permits the selection of test techniques based on the values of the metrics with respect to a particular software product.     

A practical model‐based statistical approach for generating functional test cases: application in the automotive industry

With the growing complexity of industrial software applications, industrials are looking for efficient and practical methods to validate the software. This paper develops a model‐based statistical testing approach that automatically generates online and offline test cases for embedded software. It discusses an integrated framework that combines solutions for three major software testing research questions: (i) how to select test inputs; (ii) how to predict the expected results of a test; and (iii) when to stop testing software. The automatic selection of test inputs is based on a stochastic test model that accounts for the main particularity of embedded software: time sensitivity. Software test practitioners may design one or more test models when they generate random, user‐oriented, or fault‐oriented test inputs. A formal framework integrating existing and appropriate specification techniques was developed for the design of automated test oracles (executable software specifications) and the formal measurement of functional coverage. The decision to stop testing software is based on both test coverage objectives and cost constraints. This approach was tested on two representative case studies from the automotive industry. The experiment was performed at unit testing level in a simulated environment on a host personal computer (automatic test execution). The two software functionalities tested had previously been unit tested and validated using the test design approach conventionally used in the industry. Applying the proposed model‐based statistical testing approach to these two case studies, we obtained significant improvements in performing functional unit testing in a real and complex industrial context: more bugs were detected earlier and in a shorter time. Copyright © 2012 John Wiley & Sons, Ltd.