Software Project and Quality Modelling Using Bayesian Networks

This chapter provides an introduction to the use of Bayesian Network (BN) models in Software Engineering. A short overview of the theory of BNs is included, together with an explanation of why BNs are ideally suited to dealing with the characteristics and shortcomings of typical software development environments. This theory is supplemented and illustrated using real world models that illustrate the advantages of BNs in dealing with uncertainty, causal reasoning and learning in the presence of limited data.

Constraint-Based Techniques for Software Testing

In this chapter, the authors discuss some innovative applications of artificial intelligence techniques to software engineering, in particular, to automatic test generation. Automatic testing tools translate the program under test, or its model, and the test criterion, or the test objective, into constraints. Constraint solving allows then to find a solution of the constraint solving problem and to obtain test data. The authors focus on two particular applications: model-based testing as an example of black-box testing, and all-paths test generation for C programs as a white-box testing strategy. Each application is illustrated by a running example showing how constraint-based methods allow to automatically generate test data for each strategy. They also give an overview of the main difficulties of constraint-based software testing and outline some directions for future research.

Computational Intelligence for Functional Testing

Software testing is primarily a technique for achieving some degree of software quality and to gain consumer confidence. It accounts for 50% -75% of development cost. Test case design supports effective testing but is still a human centered and labour-intensive task. The Unified Modelling language (UML) is the de-facto industrial standard for specifying software system and techniques for automatic test case generation from UML models are very much needed. While extensive research has explored the use of meta-heuristics in structural testing, few have involved its use in functional testing, particularly with respect to UML. This chapter details an approach that incorporates an anti-Ant Colony Optimisation algorithm for the automatic generation of test scenarios directly from UML Activity Diagrams, thus providing a seamless progression from design to generation of test scenarios. Owing to its anti-ant behaviour, the approach generates non-redundant test scenarios.

Artificial Intelligence in Software Engineering

Artificial intelligences techniques such as knowledge based systems, neural networks, fuzzy logic and data mining have been advocated by many researchers and developers as the way to improve many of the software development activities. As with many other disciplines, software development quality improves with the experience, knowledge of the developers, past projects and expertise. Software also evolves as it operates in changing and volatile environments. Hence, there is significant potential for using AI for improving all phases of the software development life cycle. This chapter provides a survey on the use of AI for software engineering that covers the main software development phases and AI methods such as natural language processing techniques, neural networks, genetic algorithms, fuzzy logic, ant colony optimization, and planning methods.

Supporting Quality-Driven Software Design through Intellectual Assistants

Software design decisions are usually made at early stages but have far-reaching effects regarding system organization, quality, and cost. When doing design, developers apply their technical knowledge to decide among multiple solutions, seeking a reasonable balance between functional and quality-attribute requirements. Due to the complexity of this exploration, the resulting solutions are often more a matter of developer’s experience than of systematic reasoning. It is argued that AI-based tools can assist developers to search the design space more effectively. In this chapter, the authors take a software design approach driven by quality attributes, and then present two tools that have been specifically developed to support that approach. The first tool is an assistant for exploring architectural models, while the second tool is an assistant for the refinement of architectural models into object-oriented models. Furthermore, the authors show an example of how these design assistants are combined in a tool chain, in order to ensure that the main quality attributes are preserved across the design process.

MUSTER

In this chapter they present a collaborative and situational tool called MUSTER, that has been specifically designed and developed for requirements elicitation workshops, and which utilizes, extends, and demonstrates a successful application of intelligent technologies for Computer Aided Software Engineering and Computer Aided Method Engineering. The primary objective of this tool is to improve the effectiveness and efficiency of the requirements elicitation process for software systems development, whilst addressing some of the common issues often encountered in practice through the integration of intelligent technologies. The tool also offers an example of how a group support system, coupled with artificial intelligence, can be applied to very practical activities and situations within the software development process.

From Textual Scenarios to Message Sequence Charts

Requirements engineering, the first phase of any software development project, is the Achilles’ heel of the whole development process, as requirements documents are often inconsistent and incomplete. In industrial requirements documents natural language is the main presentation means. In such documents, the system behavior is specified in the form of use cases and their scenarios, written as a sequence of sentences in natural language. For the authors of requirements documents some facts are so obvious that they forget to mention them. This surely causes problems for the requirements analyst. By the very nature of omissions, they are difficult to detect by document reviews: Facts that are too obvious to be written down at the time of document writing, mostly remain obvious at the time of review. In such a way, omissions stay undetected. This book chapter presents an approach that analyzes textual scenarios with the means of computational linguistics, identifies where actors or whole actions are missing from the text, completes the missing information, and creates a message sequence chart (MSC) including the information missing from the textual scenario. Finally, this MSC is presented to the requirements analyst for validation. The book chapter presents also a case study where scenarios from a requirement document based on industrial specifications were translated to MSCs. The case study shows feasibility of the approach.