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In this paper, we describe research results arising from a technology transfer exercise on agent-oriented requirements engineering with an industry partner. We introduce two improvements to the state-of-the-art in agent-oriented requirements engineering, designed to mitigate two problems experienced by ourselves and our industry partner: (1) the lack of systematic methods for agent-oriented requirements elicitation and modelling; and (2) the lack of prescribed deliverables in agent-oriented requirements engineering. We discuss the application of our new approach to an aircraft turnaround simulator built in conjunction with our industry partner, and show how agent-oriented models can be derived and used to construct a complete requirements package. We evaluate this by having three independent people design and implement prototypes of the aircraft turnaround simulator, and comparing the three prototypes. Our evaluation indicates that our approach is effective at delivering correct, complete, and consistent requirements that satisfy the stakeholders, and can be used in a repeatable manner to produce designs and implementations. We discuss lessons learnt from applying this approach. [ABSTRACT FROM PUBLISHER]

This paper presents a computational method that employs Natural Language Processing (NLP) and text mining techniques to support requirements engineers in extracting and modeling goals from textual documents. We developed a NLP-based goal elicitation approach within the context of KAOS goal-oriented requirements engineering method. The hierarchical relationships among goals are inferred by automatically building taxonomies from extracted goals. We use smart metering system as a case study to investigate the proposed approach. Smart metering system is an important subsystem of the next generation of power systems (smart grids). Goals are extracted by semantically parsing the grammar of goal-related phrases in abstracts of research publications. The results of this case study show that the developed approach is an effective way to model goals for complex systems, and in particular, for the research-intensive complex systems. [ABSTRACT FROM PUBLISHER]

In this paper we develop a general framework to reason about testing. The difficulty of testing is assessed in terms of the amount of tests that must be applied to determine whether the system is correct or not. Based on this criterion, five testability classes are presented and related. We also explore conditions that enable and disable finite testability, and their relation to testing hypotheses is studied. We measure how far incomplete test suites are from being complete, which allows us to compare and select better incomplete test suites. The complexity of finding that measure, as well as the complexity of finding minimum complete test suites, is identified. Furthermore, we address the reduction of testing problems to each other, that is, we study how the problem of finding test suites to test systems of some kind can be reduced to the problem of finding test suites for another kind of systems. This enables to export testing methods. In order to illustrate how general notions are applied to specific cases, many typical examples from the formal testing techniques domain are presented. [ABSTRACT FROM PUBLISHER]

We propose in this paper to consider code-smells detection as a distributed optimization problem. The idea is that different methods are combined in parallel during the optimization process to find a consensus regarding the detection of code-smells. To this end, we used Parallel Evolutionary algorithms (P-EA) where many evolutionary algorithms with different adaptations (fitness functions, solution representations, and change operators) are executed, in a parallel cooperative manner, to solve a common goal which is the detection of code-smells. An empirical evaluation to compare the implementation of our cooperative P-EA approach with random search, two single population-based approaches and two code-smells detection techniques that are not based on meta-heuristics search. The statistical analysis of the obtained results provides evidence to support the claim that cooperative P-EA is more efficient and effective than state of the art detection approaches based on a benchmark of nine large open source systems where more than 85 percent of precision and recall scores are obtained on a variety of eight different types of code-smells. [ABSTRACT FROM PUBLISHER]