Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

Randomized algorithms have been used to successfully address many different types of software engineering problems. This type of algorithms employ a degree of randomness as part of their logic. Randomized algorithms are useful for difficult problems where a precise solution cannot be derived in a deterministic way within reasonable time. However, randomized algorithms produce different results on every run when applied to the same problem instance. It is hence important to assess the effectiveness of randomized algorithms by collecting data from a large enough number of runs. The use of rigorous statistical tests is then essential to provide support to the conclusions derived by analyzing such data. In this paper, we provide a systematic review of the use of randomized algorithms in selected software engineering venues in 2009. Its goal is not to perform a complete survey but to get a representative snapshot of current practice in software engineering research. We show that randomized algorithms are used in a significant percentage of papers but that, in most cases, randomness is not properly accounted for. This casts doubts on the validity of most empirical results assessing randomized algorithms. There are numerous statistical tests, based on different assumptions, and it is not always clear when and how to use these tests. We hence provide practical guidelines to support empirical research on randomized algorithms in software engineering

A practical guide for using statistical tests to assess randomized algorithms in software engineering

We present AndroidRipper, an automated technique that tests Android apps via their Graphical User Interface (GUI). AndroidRipper is based on a user-interface driven ripper that automatically explores the app&#x2019;s GUI with the aim of exercising the application in a structured manner. We evaluate AndroidRipper on an open-source Android app. Our results show that our GUI-based test cases are able to detect severe, previously unknown, faults in the underlying code, and the structured exploration outperforms a random approach.

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Using GUI ripping for automated testing of Android applications

Randomized algorithms are widely used to address many types of software engineering problems, especially in the area of software verification and validation with a strong emphasis on test automation. However, randomized algorithms are affected by chance and so require the use of appropriate statistical tests to be properly analysed in a sound manner. This paper features a systematic review regarding recent publications in 2009 and 2010 showing that, overall, empirical analyses involving randomized algorithms in software engineering tend to not properly account for the random nature of these algorithms. Many of the novel techniques presented clearly appear promising, but the lack of soundness in their empirical evaluations casts unfortunate doubts on their actual usefulness. In software engineering, although there are guidelines on how to carry out empirical analyses involving human subjects, those guidelines are not directly and fully applicable to randomized algorithms. Furthermore, many of the textbooks on statistical analysis are written from the viewpoints of social and natural sciences, which present different challenges from randomized algorithms. To address the questionable overall quality of the empirical analyses reported in the systematic review, this paper provides guidelines on how to carry out and properly analyse randomized algorithms applied to solve software engineering tasks, with a particular focus on software testing, which is by far the most frequent application area of randomized algorithms within software engineering. Copyright © 2012 John Wiley & Sons, Ltd.

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A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering

技術解説 IEEE Computer

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 approach.

State-Based Testing of Ajax Web Applications

Model-based testing relies on the assumption that effective adequacy criteria can be defined in terms of model coverage achieved by a set of test paths. However, such test paths are only abstract test cases and input test data must be specified to make them concrete. We propose a novel approach that combines model-based and combinatorial testing in order to generate executable and effective test cases from a model. Our approach starts from a finite state model and applies model-based testing to generate test paths that represent sequences of events to be executed against the system under test. Such paths are transformed to classification trees, enriched with domain input specifications such as data types and partitions. Finally, executable test cases are generated from those trees using t-way combinatorial criteria. While test cases that satisfy a combinatorial criterion can be generated for each individual test path obtained from the model, we introduce a post-optimization algorithm that can guarantee the combinatorial criterion of choice on the whole set of test paths extracted from the model. The resulting test suite is smaller, but it still satisfies the same adequacy criterion. We developed a tool and used it to evaluate our approach on 6 subject systems of various types and sizes, to study the effectiveness of the generated test suites, the reduction achieved by the post-optimization algorithm, as well as the effort required to produce them.

Combining model-based and combinatorial testing for effective test case generation

Aspect-oriented design needs to be systematically assessed with respect to modularity flaws caused by the realization of driving system concerns, such as tangling, scattering, and excessive concern dependencies. As a result, innovative concern metrics have been defined to support quantitative analyses of concern's properties. However, the vast majority of these measures have not yet being theoretically validated and managed to get accepted in the academic or industrial settings. The core reason for this problem is the fact that they have not been built by using a clearly-defined terminology and criteria. This paper defines a concern-oriented framework that supports the instantiation and comparison of concern measures. The framework subsumes the definition of a core terminology and criteria in order to lay down a rigorous process to foster the definition of meaningful and well-founded concern measures. In order to evaluate the framework generality, we demonstrate the framework instantiation and extension to a number of concern measures suites previously used in empirical studies of aspect-oriented software maintenance.

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On the Maintainability of Aspect-Oriented Software: A Concern-Oriented Measurement Framework

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.

A Multi-Objective Technique to Prioritize Test Cases

The increasing demand for complex and distributed software calls for novel software engineering methods and techniques, to create systems able to autonomously adapt to dynamically changing situations In this paper, we present a framework for engineering requirements for adaptive software systems The approach, called Tropos4AS, combines goal-oriented concepts and high-variability design methods The Tropos4AS requirements model can be directly mapped to software prototypes with an agent-oriented architecture which can be executed for requirements validation and refinement We give a comprehensive description of the framework, with conceptual models, modelling guidelines, and supporting tools The applicability of the framework to requirements validation and refinement is illustrated through a case study Two controlled experiments with subjects provide an empirical evaluation of the proposed modelling language, with statistical evidence of the effectiveness of the modelling approach for gathering requirements of adaptive systems

Alessandro Marchetto

Papers

State-Based Testing of Ajax Web Applications

Combining model-based and combinatorial testing for effective test case generation

On the Maintainability of Aspect-Oriented Software: A Concern-Oriented Measurement Framework

A Multi-Objective Technique to Prioritize Test Cases

Engineering requirements for adaptive systems