Yansheng Lu

Regression testing for component-based software via built-in test design

Component-based software technology is expected to be an effective and widely used method of constructing software system. However, some specialties of component bring a great challenge for testing the systems built by externally-provided components, especially for regression testing. Built-in test design is a fairly effective way to improve components testability. In this paper, we present an improved regression testing method based on built-in test design for component-based systems. It needs the mutual collaboration between the component developers and users. Component developers are responsible for analyzing the affected methods and constructing the corresponding testing-interfaces in the new component version, and then component users can conveniently pick out the subset of test cases for regression testing with these testing-interfaces. Through employing preliminary experiments on some medium scale systems, our regression testing method based on built-in test design has been proven to be fairly feasible and cost-effective in practice.

Adaptive Random Test Case Generation for Combinatorial Testing

Random testing (RT), a fundamental software testing technique, has been widely used in practice. Adaptive random testing (ART), an enhancement of RT, performs better than original RT in terms of fault detection capability. However, not much work has been done on effectiveness analysis of ART in the combinatorial test spaces. In this paper, we propose a novel family of ART-based algorithms for generating combinatorial test suites, mainly based on fixed-size-candidate-set ART and restricted random testing (that is, ART by exclusion). We use an empirical approach to compare the effectiveness of test sets obtained by our proposed methods and random selection strategy. Experimental data demonstrate that the ART-based tests cover all possible combinations at a given strength more quickly than randomly chosen tests, and often detect more failures earlier and with fewer test cases in simulations.

Image retrieval based on multi-concept detector and semantic correlation

With the rapid development of future network, there has been an explosive growth in multimedia data such as web images. Hence, an efficient image retrieval engine is necessary. Previous studies concentrate on the single concept image retrieval, which has limited practical usability. In practice, users always employ an Internet image retrieval system with multi-concept queries, but, the related existing approaches are often ineffective because the only combination of single-concept query techniques is adopted. At present semantic concept based multi-concept image retrieval is becoming an urgent issue to be solved. In this paper, a novel Multi-Concept image Retrieval Model (MCRM) based on the multi-concept detector is proposed, which takes a multi-concept as a whole and directly learns each multi-concept from the rearranged multi-concept training set. After the corresponding retrieval algorithm is presented, and the log-likelihood function of predictions is maximized by the gradient descent approach. Besides, semantic correlations among single-concepts and multiconcepts are employed to improve the retrieval performance, in which the semantic correlation probability is estimated with three correlation measures, and the visual evidence is expressed by Bayes theorem, estimated by Support Vector Machine (SVM). Experimental results on Corel and IAPR data sets show that the approach outperforms the state-of-the-arts. Furthermore, the model is beneficial for multi-concept retrieval and difficult retrieval with few relevant images.摘要创新点随着未来网络的快速发展, 可以预见 Web 图像等多媒体数据呈现爆炸式增长, 因此, 亟需一种高效的图像检索引擎. 已有研究主要关注单概念图像检索方式, 这弱化了实际可用性. 事实上, 用户使用图像检索系统, 多数以多概念检索为主. 为了克服该缺点, 已有的语义检索方法采用了单概念检索方法去完成多概念检索, 然而, 单概念检索方法并未考虑多概念场景语境, 导致检索结果常常是低效的. 当前, 基于语义概念的多概念图像检索成为一个亟待解决的研究问题. 本文提出一种基于多概念检测器的多概念图像检索方法 MCRM, 它把一个场景多概念当做一个有语境的整体, 而直接从重新整理的多概念训练集中学习出来, MCRM 检索方法首先提出了一种检索算法, 然后通过梯度下降法极大化似然函数. 此外, 在单概念和场景多概念之间的语义关联也被用来提升多概念检索的性能. 为了衡量多概念语义关联, MCRM 方法提出了三种估算语义关联概率的方法, 而场景多概念是否存在于图像中的视觉证据被贝叶斯规则转换后交由支持向量机去概率估算. 实验表明: MCRM 方法在多概念图像检索和相关图像稀少的 “困难检索” 上优势明显.

Prioritizing Variable-Strength Covering Array

Combinatorial interaction testing is a well-studied testing strategy, and has been widely applied in practice. Combinatorial interaction test suite, such as fixed-strength and variable-strength interaction test suite, is widely used for combinatorial interaction testing. Due to constrained testing resources in some applications, for example in combinatorial interaction regression testing, prioritization of combinatorial interaction test suite has been proposed to improve the efficiency of testing. However, nearly all prioritization techniques may only support fixed-strength interaction test suite rather than variable-strength interaction test suite. In this paper, we propose two heuristic methods in order to prioritize variable-strength interaction test suite by taking advantage of its special characteristics. The experimental results show that our methods are more effective for variable-strength interaction test suite by comparing with the technique of prioritizing combinatorial interaction test suites according to test case generation order, the random test prioritization technique, and the fixed-strength interaction test suite prioritization technique. Besides, our methods have additional advantages compared with the prioritization techniques for fixed-strength interaction test suite.

Prioritization of combinatorial test cases by incremental interaction coverage

Combinatorial testing is a well-recognized testing method, and has been widely applied in practice. To facilitate analysis, a common approach is to assume that all test cases in a combinatorial test suite have the same fault detection capability. However, when testing resources are limited, the order of executing the test cases is critical. To improve testing cost-effectiveness, prioritization of combinatorial test cases is employed. The most popular approach is based on interaction coverage, which prioritizes combinatorial test cases by repeatedly choosing an unexecuted test case that covers the largest number on uncovered parameter value combinations of a given strength (level of interaction among parameters). However, this approach suffers from some drawbacks. Based on previous observations that the majority of faults in practical systems can usually be triggered with parameter interactions of small strengths, we propose a new strategy of prioritizing combinatorial test cases by incrementally adjusting the strength values. Experimental results show that our method performs better than the random prioritization technique and the technique of prioritizing combinatorial test suites according to test case generation order, and has better performance than the interaction-coverage-based test prioritization technique in most cases.

Adaptive random prioritization for interaction test suites

Combinatorial interaction testing (CIT), a black-box testing method, has been well studied in recent years. It aims at constructing an effective interaction test suites, so as to identify the faults that are caused by interactions among parameters. After interaction test suites are generated by CIT, the execution order of test cases in the test suite becomes critical due to limited testing resources. To determine test case order, the prioritization of interaction test suites has been employed. As we know, random prioritization (RP) of test cases has been considered as simple but ineffective. Existing research unveils that adaptive random prioritization (ARP) of test cases is an alternative and promising candidate that may replace RP. However, previous ARP techniques may not be used to prioritize interaction test suites due to the lack of source-code-related information in interaction test suite, such as statement coverage, function coverage, or branch coverage. In this paper, we not only propose the ARP strategy in order to prioritize interaction test suites by using interaction coverage information, without the source-code-related information, but also unify the RP strategy and traditional interaction-coverage based prioritization strategy (ICBP). Additionally, simulation studies indicate that the ARP strategy performs better than the RP strategy, test-case-generation prioritization, and reverse test-case-generation prioritization, and can also be more time-saving than ICBP while greatly maintaining similar, or even better, effectiveness.

Testing approach of component security based on dynamic monitoring

The reliability and security of software components inhibits the further development of component technology. Enhancing the testing ability of components is very important in components-based software engineering. This paper proposes a testing approach of component security (TACS) based on a dynamic monitoring and detecting algorithm CSVD (component security vulnerability detecting) and discusses the dynamic monitoring mechanism, testing approach and detecting algorithm. In addition, Punylib.dll, a third-party component, is analyzed using TACS for its security analysis. The case study shows that TACS has good integrity, validity and better operability.

Using Dependence Matrix to Support Change Impact Analysis for CBS

Component-based software development technique and its extensive application have led to the wide research in various aspects of component-based software (CBS). The rapid evolution of CBS brings great challenges to its maintenance in the later phase, so it is quite necessary to measure the change impact on the whole system. By using component dependence matrix to represent component-based software system, the paper mainly discusses the case of component modification, including the single component change and the changes of multi-components, and proposes the corresponding algorithms (i.e. Appxm RM, Generate_SP, and Calculate_CR) for analyzing the change impacts in CBS. In addition, the calculation process of change impact analysis and its effectiveness are also validated by a simple CBS.

Testing Approach of Component Security Based on Fault Injection

Component-Based Software Engineering (CBSE) has been the research focus in the field of software engineering at present. But problems with the reliability and security of components have not yet been resolved, which worried the component developer and user. Testing the software components is an important approach which guarantees and enhances their reliability and security. This paper proposes a testing approach of component security based on fault injection (TAFI), and then defines and discusses requirement specification of components security and fault injection model. In addition, 31 software components are analyzed using our approach based on fault injection model. The case study shows that our approach is effective and operable.

COMPONENT SECURITY TESTING APPROACH BASED ON EXTENDED CHEMICAL ABSTRACT MACHINE

Unreliable component security hinders the development of component technology. Component security testing is rarely researched with comprehensive focus; several approaches or technologies for detec...