Statistical Debugging Effectiveness as a Fault Localization Approach: Comparative Study

Fault localization is an important topic in software testing, as it enables the developer to specify fault location in their code. One of the dynamic fault localization techniques is statistical debugging. In this study, two statistical debugging algorithms are implemented, SOBER and Cause Isolation, and then the experimental works are conducted on five programs coded using Python as an example of well-known dynamic programming language. Results showed that in programs that contain only single bug, the two studied statistical debugging algorithms are very effective to localize a bug. In programs that have more than one bug, SOBER algorithm has limitations related to nested predicates, rarely observed predicates and complement predicates. The Cause Isolation has limitations related to sorting predicates based on importance and detecting bugs in predicate condition. The accuracy of both SOBER and Cause Isolation is affected by the program size. Quality comparison showed that SOBER algorithm requires more code examination than Cause Isolation to discover the bugs.

Cooperative Software Testing and Analysis: Advances and Challenges

In recent years, to maximize the value of software testing and analysis, we have proposed the methodology of cooperative software testing and analysis （in short as cooperative testing and analysis） to enable testing and analysis tools to cooperate with their users （in the form of tool-human cooperation）, and enable one tool to cooperate with another tool （in the form of tool-tool cooperation）. Such cooperations are motivated by the observation that a tool is typically not powerful enough to address complications in testing or analysis of complex real-world software, and the tool user or another tool may be able to help out some problems faced by the tool. To enable tool-human or tool-tool cooperation, effective mechanisms need to be developed 1） for a tool to communicate problems faced by the tool to the tool user or another tool, and 2） for the tool user or another tool to assist the tool to address the problems. Such methodology of cooperative testing and analysis forms a new research frontier on synergistic cooperations between humans and tools along with cooperations between tools and tools. This article presents recent example advances and challenges on cooperative testing and analysis.

Improving Fault Detection in Modified Code-A Study from the Telecommunication Industry

Many software systems are developed in a number of consecutive releases. In each release not only new code is added but also existing code is often modified. In this study we show that the modified code can be an important source of faults. Faults are widely recognized as one of the major cost drivers in software projects. Therefore, we look for methods that improve the fault detection in the modified code. We propose and evaluate a number of prediction models that increase the efficiency of fault detection. To build and evaluate our models we use data collected from two large telecommunication systems produced by Ericsson. We evaluate the performance of our models by applying them both to a different release of the system than the one they are built on and to a different system. The performance of our models is compared to the performance of the theoretical best model, a simple model based on size, as well as to analyzing the code in a random order （not using any model）. We find that the use of our models provides a significant improvement over not using any model at all and over using a simple model based on the class size. The gain offered by our models corresponds to 38-57% of the theoretical maximum gain.