Modeling and Testing of Aspect-Oriented Systems

Aspect-Oriented programming modularizes crosscutting concerns into Aspects, which are automatically weaved to the specified points of a program. Although Aspect-Oriented programming improves program maintainability and the encapsulation of crosscutting concerns, it also breaks some traditional programming rules due to the weaving mechanism. Therefore, a new software testing approach has to be developed to rigorously test Aspect-Oriented programs. In this thesis, we introduce the concept of Aspect modeling and Aspect testing and then proceed to our investigation of a model-based incremental approach for testing Aspect-Oriented programs. First, a state machine model in UML is created for each Aspect and each base class, which is the class to be weaved with the aspect. Then each individual aspect or base class is tested using the test cases generated from state machine models. A combined state machine model is established by weaving the aspect model into the base class model. Finally, we perform a test on the woven program using test cases generated from the combined state machine model. Because the number of scenarios for weaving aspects and base classes could be very large, it may require a huge number of test cases to effectively test the program. To speed up the process, we propose a prioritizing strategy for selecting test cases in order to find errors sooner since different test cases have different capacity for tracking errors. We demonstrate that the test cases generated from the state machine model have to satisfy the adequacy of the transition coverage, the round-trip coverage, and the state coverage in the state machine model. Furthermore, the prioritizing strategy is developed based on the number of changes brought by weaving of an aspect and its base classes. The test case including more changes will have a higher priority. The effectiveness of the investigated strategy is evaluated through the case study and the mutation testing. The result of case study shows that the model-based incremental approach integrated with prioritizing test case selection provides an effective tool for testing large-scale Aspect-Oriented systems.  M.S

Using Evolutionary Mutation Testing to improve the quality of test suites

Mutation testing is a method used to assess and improve the fault detection capability of a test suite by creating faulty versions, called mutants, of the system under test. Evolutionary Mutation Testing (EMT), like selective mutation or mutant sampling, was proposed to reduce the computational cost, which is a major concern when applying mutation testing. This technique implements an evolutionary algorithm to produce a reduced subset of mutants but with a high proportion of mutants that can help the tester derive new test cases (strong mutants). In this paper, we go a step further in estimating the ability of this technique to induce the generation of test cases. Instead of measuring the percentage of strong mutants within the subset of generated mutants, we compute how much the test suite is actually improved thanks to those mutants. In our experiments, we have compared the extent to which EMT and the random selection of mutants help to find missing test cases in C++ object-oriented systems. We can conclude from our results that the percentage of mutants generated with EMT is lower than with the random strategy to obtain a test suite of the same size and that the technique scales better for complex programs

A Test-Driven Approach to Developing Pointcut Descriptors in AspectJ

International audienceAspect-oriented programming (AOP) languages introduce new constructs that can lead to new types of faults, which must be targeted by testing techniques. In particular, AOP languages such as AspectJ use a pointcut descriptor (PCD) that provides a convenient way to declaratively specify a set of joinpoints in the program where the aspect should be woven. However, a major difficulty when testing that the PCD matches the intended set of joinpoints is the lack of precise specification for this set other than the PCD itself. In this paper, we propose a test-driven approach for the development and validation of the PCD. We developed a tool, AdviceTracer, which enriches the JUnit API with new types of assertions that can be used to specify the expected joinpoints. In order to validate our approach, we also developed a mutation tool that systematically injects faults into PCDs. Using these two tools, we perform experiments to validate that our approach can be applied for specifying expected joinpoints and for detecting faults in the PCD

VERDICTS: Visual Exploratory Requirements Discovery and Injection for Comprehension and Testing of Software

We introduce a methodology and research tools for visual exploratory software analysis. VERDICTS combines exploratory testing, tracing, visualization, dynamic discovery and injection of requirements specifications into a live quick-feedback cycle, without recompilation or restart of the system under test. This supports discovery and verification of software dynamic behavior, software comprehension, testing, and locating the defect origin. At its core, VERDICTS allows dynamic evolution and testing of hypotheses about requirements and behavior, by using contracts as automated component verifiers. We introduce Semantic Mutation Testing as an approach to evaluate concordance of automated verifiers and the functional specifications they represent with respect to existing implementation. Mutation testing has promise, but also has many known issues. In our tests, both black-box and white-box variants of our Semantic Mutation Testing approach performed better than traditional mutation testing as a measure of quality of automated verifiers

LittleDarwin: a Feature-Rich and Extensible Mutation Testing Framework for Large and Complex Java Systems

Mutation testing is a well-studied method for increasing the quality of a test suite. We designed LittleDarwin as a mutation testing framework able to cope with large and complex Java software systems, while still being easily extensible with new experimental components. LittleDarwin addresses two existing problems in the domain of mutation testing: having a tool able to work within an industrial setting, and yet, be open to extension for cutting edge techniques provided by academia. LittleDarwin already offers higher-order mutation, null type mutants, mutant sampling, manual mutation, and mutant subsumption analysis. There is no tool today available with all these features that is able to work with typical industrial software systems.Comment: Pre-proceedings of the 7th IPM International Conference on Fundamentals of Software Engineerin

XPA: An Open Source IDE for XACML Policies

This paper presents XPA (XACML Policy Analyzer), an open source IDE (Integrated Development Environment) for testing, debugging, and mutating XACML 3.0 policies. XACML is an OASIS standard for specifying attributebased access control policies. XPA provides a variety of new techniques for generating test cases from policies, localizing bugs in faulty policies, and repairing faulty policy elements. XPA has been applied to numerous XACML policies from the literature and real-world applications. These policies have been used to quantitatively evaluate the effectiveness of various testing and debugging methods. For system developers and administrators, XPA is a practical IDE for developing dependable XACML policies. For access control researchers, XPA offers a versatile toolkit for studying and evaluating new testing, debugging, and verification techniques