Ajax supports the development of rich-client Web applications, by providing primitives for the execution of asynchronous requests and for the dynamic update of the page structure and content. Often, Ajax Web applications consist of a single page whose elements are updated in response to callbacks activated asynchronously by the user or by a server message. These features give rise to new kinds of faults that are hardly revealed by existing Web testing approaches. In this paper, we propose a novel state-based testing approach, specifically designed to exercise Ajax Web applications. The Document Object Model (DOM) of the page manipulated by the Ajax code is abstracted into a state model. Callback executions triggered by asynchronous messages received from the Web server are associated with state transitions. Test cases are derived from the state model based on the notion of semantically interacting events. We evaluate the approach on a case study in terms of fault revealing capability. We also measure the amount of manual interventions involved in constructing and refining the model required by this approac

No abstract

While performing regression testing, an appropriate choice for test case ordering allows the tester to early discover faults in source code. To this end, test case prioritization techniques can be used. Several existing test case prioritization techniques leave out the execution cost of test cases and exploit a single objective function (e.g., code or requirements coverage). In this paper, we present a multi-objective test case prioritization technique that determines the ordering of test cases that maximize the number of discovered faults that are both technical and business critical. In other words, our new technique aims at both early discovering faults and reducing the execution cost of test cases. To this end, we automatically recover links among software artifacts (i.e., requirements specifications, test cases, and source code) and apply a metric-based approach to automatically identify critical and fault-prone portions of software artifacts, thus becoming able to give them more importance during test case prioritization. We experimentally evaluated our technique on 21 Java applications. The obtained results support our hypotheses on efficiency and effectiveness of our new technique and on the use of automatic artifacts analysis and weighting in test case prioritization.

The increasing demand for reliable Web applications gives a central role to Web testing. Most of the existing works are focused on the definition of novel testing techniques, specifically tailored to the Web. However, no attempt was carried out so far to understand the specific nature of Web faults. This is of fundamental importance to assess the effectiveness of the proposed Web testing techniques. In this paper, we describe the process followed in the construction of a Web fault taxonomy. After the initial, top-down construction, the taxonomy was subjected to four iterations of empirical validation aimed at refining it and at understanding its effectiveness in bug classification. The final taxonomy is publicly available for consultation and editing on a Wiki page. Testers can use it in the definition of test cases that target specific classes of Web faults. Researchers can use it to build fault seeding tools that inject artificial faults which resemble the real ones

No abstract

Several techniques and tools have been proposed for the automatic generation of test cases. Usually, these tools are evaluated in terms of fault-revealing or coverage capability, but their impact on the manual debugging activity is not considered. The question is whether automatically generated test cases are equally effective in supporting debugging as manually written tests. We conducted a family of three experiments (five replications) with humans (in total, 55 subjects) to assess whether the features of automatically generated test cases, which make them less readable and understandable (e.g., unclear test scenarios, meaningless identifiers), have an impact on the effectiveness and efficiency of debugging. The first two experiments compare different test case generation tools (Randoop vs. EvoSuite). The third experiment investigates the role of code identifiers in test cases (obfuscated vs. original identifiers), since a major difference between manual and automatically generated test cases is that the latter contain meaningless (obfuscated) identifiers. We show that automatically generated test cases are as useful for debugging as manual test cases. Furthermore, we find that, for less experienced developers, automatic tests are more useful on average due to their lower static and dynamic complexity.

No abstract

No abstract