Supporting the software testing process through specification animation

Achieving consistency between a specification and its implementation is an important part of software development. In this paper, we present a method for generating passive test oracles that act as self-checking implementations. The implementation is verified using an animation tool to check that the behavior of the implementation matches the behavior of the specification. We discuss how to integrate this method into a framework developed for systematically animating specifications, which means a tester can significantly reduce testing time and effort by reusing work products from the animation. One such work product is a testgraph: a directed graph that partially models the states and transitions of the specification. Testgraphs are used to generate sequences for animation, and during testing, to execute these same sequences on the implementation.

Teaching software engineering fundamentals to practicing engineers

This paper describes an ongoing collaboration between Boeing Australia Limited and the University of Queensland to develop and deliver an introductory course on software engineering for Boeing Australia. The aim of the course is to provide a common understanding for all Boeing Australia's engineering staff of the nature of software engineering and the practices used throughout Boeing Australia. It is meant as an introductory course that can be presented to people with varying backgrounds, such as recent software engineering graduates, systems engineers, quality assurance personnel, etc. The paper describes the structure and content of the course, and the evaluation techniques used to collect feedback from the participants and the corresponding results. The course has been well-received by the participants, but the feedback from the course has indicated a need for more advanced courses in specific areas.

Off-line signature verification and forgery detection system based on fuzzy modeling

This paper presents an innovative approach for signature verification and forgery detection based on fuzzy modeling. The signature image is binarized and resized to a fixed size window and is then thinned. The thinned image is then partitioned into a fixed number of eight sub-images called boxes. This partition is done using the horizontal density approximation approach. Each sub-image is then further resized and again partitioned into twelve further sub-images using the uniform partitioning approach. The features of consideration are normalized vector angle (α) from each box. Each feature extracted from sample signatures gives rise to a fuzzy set. Since the choice of a proper fuzzification function is crucial for verification, we have devised a new fuzzification function with structural parameters, which is able to adapt to the variations in fuzzy sets. This function is employed to develop a complete forgery detection and verification system.