Chuanqi Tao

Change Impact Analysis Based on a Taxonomy of Change Types

Software change impact analysis (CIA) is a key technique for identifying unpredicted and potential effects caused by changes made to software. Different change types often have different impact mechanisms, even some changes do not impact other entities in programs in spite of some dependences existed between these entities and the modified entity. In this paper, we propose a static CIA technique, which considers different impact mechanisms and rules of different change types, to calculate the impact sets. Precision improvement of the impact sets relies on 3 aspects: change types of a modified entity, dependences between the modified entity and other entities, and the intuition that to win at the start -- if the initial impact set is estimated more accurately, then the final impact set depending on this initial impact set will be more precise. Experimental case study demonstrates the effectiveness of our technique, and its potential applications in software maintenance.

Using lattice of class and method dependence for change impact analysis of object oriented programs

Software change impact analysis (CIA) is a key technique to identify unpredicted and potential effects caused by software changes. In this paper, we propose a new CIA technique based on a compact and effective representation for object oriented programs, called lattice of class and method dependence (LoCMD). This novel representation can effectively capture the dependences between classes and methods. Based on the LoCMD, our CIA technique calculates a ranked list of potential impacted methods according to a metric, impact factor, which corresponds to the priority of these methods to be inspected. Initial case study validates the reasonability of our two assumptions, and demonstrates the effectiveness of our technique.

An Approach to Regression Test Selection Based on Hierarchical Slicing Technique

Regression testing is an important stage of software maintenance. Regression test selection is a key technique to test case reuse. Program slicing is one of these commonly used regression test selection techniques. The proposed slicing techniques for regression test selection are primarily for procedural program. Hierarchy slice, which is used for object-oriented programs according to the logical hierarchy of program, is able to compute slice step by step based on hierarchical slicing criterion. Compared to traditional slicing, hierarchical slicing technique can produce more precise slice especially for object-oriented program. This paper applies hierarchical slicing technique to regression test selection in order to improve the precision of regression test selection and address the problem of level. The approach computes hierarchy slice on the modified parts of program, then selects test cases from different levels in terms of test case coverage. This approach can select test cases from high level to low level of program. The initial experimental results present the effectiveness of applying hierarchical slicing in regression test selection.

A Hierarchical Model for Regression Test Selection and Cost Analysis of Java Programs

Regression testing is an important but expensive stage of software maintenance. Regression test selection addresses the problem of reducing testing cost through selecting a subset of the existing test cases or rerun. Cost-effectiveness is an indispensable factor to consider when developing a regression testing technique. Cost models are created for the purpose of assessing cost-effectiveness of these techniques. The current regression test selection strategies or cost analysis seldom consider hierarchy, which is an inherent characteristic of object-oriented program. In addition, selecting test cases at different levels influences the precision and efficiency of selection. This paper presents a hierarchical regression test selection technique for Java programs to stepwise select test cases from high level of program to low level of program. To effectively evaluate the correlative overall cost, this paper also proposes a hierarchical cost model for analyzing the cost-effectiveness of selection level according to hierarchy step by step. The empirical studies show that our approach can reduce the total cost and achieve more cost-effective results.

Regression Testing of Component-Based Software: A Systematic Practise Based on State Testing

Component-based software systems consist of various components, such as third-party components and in-house built components. Component changes frequently occur in software maintenance, which refers to regression testing. When changes made to a component, the component could be affected, moreover, the changes could bring impacts on the entire system. Related existing research did not address the issue of systematic regression testing of component-based software, especially at system level. This paper proposes a systematic regression testing method from components to system, including analysis of change, impact and test suite refreshment. The paper also reports a case study based on a realistic component-based software system using a state-based testing practice, which shows that the approach is feasible and effective.

An Approach to Mobile Application Testing Based on Natural Language Scripting.

With the rapid advance of mobile computing technology and wireless networking, there is a significant increase of mobile subscriptions. This brings new business requirements and demands in mobile software testing, and causes new issues and challenges in mobile testing and automation. As there are multiple platforms for diverse devices, engineers suffer from the different scripting languages to write platform-specific test scripts. In addition, a unified automation infrastructure is not offered with the existing test platform. This paper proposes a novel approach to mobile application testing based on natural language scripting. A Java-based test script generation approach is developed to support executable test script generation based on the given natural language-based mobile app test operation scripts. A prototype tool is implemented based on some open sources. Finally, the paper reports empirical studies to indicate the feasibility and effectiveness of the proposed approach.

A Practical Study on Quality Evaluation for Age Recognition Systems.

Face recognition system is a widely-used intelligent application nowadays. Existing recognition system evaluation methods primarily focus on recognition rate, i.e., the correct result. However, current research seldom focuses on the quality evaluation of face recognition systems. They seldom consider accuracy or the quality of recognition. To address this issue, this paper proposes several quality factors for evaluation. In addition, corresponding metrics for diverse quality factors are illustrated. Moreover, the paper presents an experimental study on a realistic non-trial face age recognition system using the proposed quality evaluation method. The study result shows the proposed method is feasible and effectiveness in quality evaluation.