Context:The Web has had a significant impact on all aspects of our society. As our society relies more and more on the Web, the dependability of web applications has become increasingly important. To make these applications more dependable, for the past decade researches have proposed various techniques for testing web-based software applications. Our literature search for related studies retrieved 147 papers in the area of web application testing, which have appeared between 2000-2011. Objective: As this research area matures and the number of related papers increases, it is important to systematically identify, analyze, and classify the publications and provide an overview of the trends in this specialized field. Method: We review and structure the body of knowledge related to web application testing through a systematic mapping (SM) study. As part of this study, we pose two sets of research questions, define selection and exclusion criteria, and systematically develop and refine a classification schema. In addition, we conduct a bibliometrics analysis of the papers included in our study. Results: Our study includes a set of 79 papers (from the 147 retrieved papers) published in the area of web application testing between 2000-2011. We present the results of our systematic mapping study. Our mapping data is available through a publiclyaccessible repository. We derive the observed trends, for instance, in terms of types of papers, sources of information to derive test cases, and types of evaluations used in papers. We also report the demographics and bibliometrics trends in this domain, including top-cited papers, active countries and researchers, and top venues in this research area. Conclusion: We discuss the emerging trends in web application testing, and discuss the implications for researchers and practitioners in this area. The results of our systematic mapping can help researchers to obtain an overview of existing web application testing approaches and indentify areas in the field that require more attention from the research community.

Mutation testing is an effective test adequacy assessment technique. However, it suffers from two main issues. First, there is a high computational cost in executing the test suite against a potentially large pool of generated mutants. Second, there is much effort involved in filtering out equivalent mutants, which are syntactically different but semantically identical to the original program. Prior work has mainly focused on detecting equivalent mutants after the mutation generation phase, which is computationally expensive and has limited efficiency. In this paper, we propose a technique that leverages static and dynamic program analysis to guide the mutation generation process a-priori towards parts of the code that are error-prone or likely to influence the program's output. Further, we focus on the JavaScript language, and propose a set of mutation operators that are specific to web applications. We implement our approach in a tool called MUTANDIS. We empirically evaluate MUTANDIS on a number of web applications to assess the efficacy of the approach.

Mutation testing is an effective test adequacy assessment technique. However, there is a high computational cost in executing the test suite against a potentially large pool of generated mutants. Moreover, there is much effort involved in filtering out equivalent mutants. Prior work has mainly focused on detecting equivalent mutants after the mutation generation phase, which is computationally expensive and has limited efficiency. We propose an algorithm to select variables and branches for mutation as well as a metric, called F unctionRank, to rank functions according to their relative importance from the application's behaviour point of view. We present a technique that leverages static and dynamic analysis to guide the mutation generation process towards parts of the code that are more likely to influence the program's output. Further, we focus on the JavaScript language, and propose a set of mutation operators that are specific to web applications. We implement our approach in a tool called MUTANDIS. The results of our empirical evaluation show that (1) more than 93% of generated mutants are non-equivalent, and (2) more than 75% of the surviving non-equivalent mutants are in the top 30% of the ranked functions.

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The event-driven and highly dynamic nature of JavaScript, as well as its runtime interaction with the Document Object Model (DOM) make it challenging to test JavaScript-based applications. Current web test automation techniques target the generation of event sequences, but they ignore testing the JavaScript code at the unit level. Further they either ignore the oracle problem completely or simplify it through generic soft oracles such as HTML validation and runtime exceptions. We present a framework to automatically generate test cases for Java-Script applications at two complementary levels, namely events and individual JavaScript functions. Our approach employs a combination of function coverage maximization and function state abstraction algorithms to efficiently generate test cases. In addition, these test cases are strengthened by automatically generated mutation-based oracles. We empirically evaluate the implementation of our approach, called JSEFT, to assess its efficacy. The results, on 13 JavaScript-based applications, show that the generated test cases achieve a coverage of 68% and that JSEFT can detect injected JavaScript and DOM faults with a high accuracy (100% precision, 70% recall). We also find that JSEFT outperforms an existing JavaScript test automation framework both in terms of coverage and detected faults.The results of our evaluation show that on average (1) the generated test suite by JSEFT achieves a 68% JavaScript code coverage, (2) compared to ARTEMIS, a feedback-directed JavaScript testing framework [2], JSEFT achieves 53% better coverage, and (3) the test oracles generated by JSEFT are able to detect injected faults with 100% precision and 70% recall. II. RELATED WORKWeb application testing. Marchetto and Tonella [3] propose a search-based algorithm for generating event-based sequences