In mobile application development, the frequentsoftware release limits the testing time resource. In order todetect bugs in early phases, researchers proposed various testcase prioritization (TCP) techniques in past decades. In practice, considering that some test case is described or contains text, theresearchers also employed Natural Language Processing (NLP)to assist the TCP techniques. This paper conducted an extensiveempirical study to analyze the performance of three NLP basedTCP technologies, which is based on 15059 test cases from 30industrial projects. The result shows that all of these threestrategies can help to improve the efficiency of software testing, and the Risk strategy achieved the best performance across thesubject programs.

Any program may contain more than one fault, and these faults may interfere with each other in a variety of ways.Software behavior may be affected by the interference, resulting in some uncertain results.Such results have negative impact on many software engineering tasks, including regression testing, fault localization, debugging, fault clustering etc.Therefore, understanding the interference becomes an important topic.This paper investigates the fault interference from the perspective of software construction.We introduce the coupling of software construction in order to explain the reasons for fault interference.We observed that different types of coupling may cause three kinds of fault interference and have different probabilities to make the software strike the fault interference traps.We conducted a preliminary experiment on four industrial programs.The results show that our approach gives a good explanation on fault interference.

Symbolic execution maintains a path condition pc for every possible path of a program.It is challenging to construct a pc if some complex issues are involved in the path.A predicate interpretation pi is a subexpression of a pc and a pc of a path is a conjunction of all pis in the path.Predicate interpretation has been widely used in theoretical analysis on domain testing and related fields.It recently emerges new impact on software testing by using partial path constraints to generate test data.In this paper, we propose an approach to produce pis in a program.A tool for predicate interpretation analysis for Java programs is implemented based on the data-flow framework of Soot.Most of Java features can be handled in our tool.Moreover, a formal rule of predicate interpretation analysis is presented for more applications in the future.The experimental results show that our tool can produce pis of a program effectively and efficiently.

In many fields of software engineering, studies on human behavior have attracted a lot of attention; however, few such studies exist in automated debugging. Parnin and Orso conducted a pioneering study comparing the performance of programmers in debugging with and without a ranking-based fault localization technique, namely Spectrum-Based Fault Localization (SBFL). In this paper, we revisit the actual helpfulness of SBFL, by addressing some major problems that were not resolved in Parnin and Orso's study. Our investigation involved 207 participants and 17 debugging tasks. A user-friendly SBFL tool was adopted. It was found that SBFL tended not to be helpful in improving the efficiency of debugging. By tracking and analyzing programmers' focus of attention, we characterized their source code navigation patterns and provided in-depth explanations to the observations. Results indicated that (1) a short "first scan" on the source code tended to result in inefficient debugging; and (2) inspections on the pinpointed statements during the "follow-up browsing" were normally just quick skimming. Moreover, we found that the SBFL assistance may even slightly weaken programmers' abilities in fault detection. Our observations imply interference between the mechanism of automated fault localization and the actual assistance needed by programmers in debugging. To resolve this interference, we provide several insights and suggestions.