94 research outputs found
FORTEST: Formal methods and testing
Formal methods have traditionally been used for specification and development of software. However there are potential benefits for the testing stage as well. The panel session associated with this paper explores the usefulness
or otherwise of formal methods in various contexts for improving software testing. A number of different possibilities for the use of formal methods are explored and questions raised. The contributors are all members of the UK FORTEST Network on formal methods and testing. Although
the authors generally believe that formal methods
are useful in aiding the testing process, this paper is intended to provoke discussion. Dissenters are encouraged to put their views to the panel or individually to the authors
AD2US: An Automated Approach to Generating Usage Scenarios from UML Activity Diagrams
Although attention has been given to the use of UML (Unified Modelling Language) activity diagrams in the generation of scenarios, thin-threads and test-cases, the processes described in the literature rely heavily on manual intervention either in the information extraction process or in the process of transforming them to an alternate structure. This paper introduces an approach that capture, store and output usage scenarios derived automatically from UML activity diagrams
A Product Oriented Modelling Concept: Holons for systems synchronisation and interoperability
Nowadays, enterprises are confronted to growing needs for traceability,
product genealogy and product life cycle management. To meet those needs, the
enterprise and applications in the enterprise environment have to manage flows
of information that relate to flows of material and that are managed in shop
floor level. Nevertheless, throughout product lifecycle coordination needs to
be established between reality in the physical world (physical view) and the
virtual world handled by manufacturing information systems (informational
view). This paper presents the "Holon" modelling concept as a means for the
synchronisation of both physical view and informational views. Afterwards, we
show how the concept of holon can play a major role in ensuring
interoperability in the enterprise context
S. N, PD Shenoy, KR Venugopal, and LM Patnaik. Moving vehicle identification using background registration technique for traffic surveillance
Real-time segmentation of moving regions in image
sequences is a fundamental step in many vision systems
including automated visual surveillance and human-machine
interface. In this paper we present a framework for detecting
some important but unknown knowledge like vehicle
identification and traffic flow count. The objective is to
monitor activities at traffic intersections for detecting
congestions, and then predict the traffic flow which assists in
regulating traffic. The present algorithm for vision-based
detection and counting of vehicles in monocular image
sequences for traffic scenes are recorded by a stationary
camera. The method is based on the establishment of
correspondences between regions and vehicles, as the vehicles
move through the image sequence. Background subtraction is
used which improves the adaptive background mixture model
and makes the system learn faster and more accurately, as well
as adapt effectively to changing environments. The resulting
system robustly identifies vehicles at intersection, rejecting
background and tracks vehicles over a specific period of time.
Real-life traffic video sequences are used to illustrate the
effectiveness of the proposed algorithm
Recommended from our members
Using formal methods to support testing
Formal methods and testing are two important approaches that assist in the development of high quality software. While traditionally these approaches have been seen as rivals, in recent
years a new consensus has developed in which they are seen as complementary. This article reviews the state of the art regarding ways in which the presence of a formal specification can be used to assist testing
Moving Vehicle Identification using Background Registration Technique for Traffic Surveillance
Real-time segmentation of moving regions in image
sequences is a fundamental step in many vision systems
including automated visual surveillance and human-machine
interface. In this paper we present a framework for detecting
some important but unknown knowledge like vehicle
identification and traffic flow count. The objective is to
monitor activities at traffic intersections for detecting
congestions, and then predict the traffic flow which assists in
regulating traffic. The present algorithm for vision-based
detection and counting of vehicles in monocular image
sequences for traffic scenes are recorded by a stationary
camera. The method is based on the establishment of
correspondences between regions and vehicles, as the vehicles
move through the image sequence. Background subtraction is
used which improves the adaptive background mixture model
and makes the system learn faster and more accurately, as well
as adapt effectively to changing environments. The resulting
system robustly identifies vehicles at intersection, rejecting
background and tracks vehicles over a specific period of time.
Real-life traffic video sequences are used to illustrate the
effectiveness of the proposed algorithm
Fine-Grained Static Detection of Obfuscation Transforms Using Ensemble-Learning and Semantic Reasoning
International audienceThe ability to efficiently detect the software protections used is at a prime to facilitate the selection and application of adequate deob-fuscation techniques. We present a novel approach that combines semantic reasoning techniques with ensemble learning classification for the purpose of providing a static detection framework for obfuscation transformations. By contrast to existing work, we provide a methodology that can detect multiple layers of obfuscation, without depending on knowledge of the underlying functionality of the training-set used. We also extend our work to detect constructions of obfuscation transformations, thus providing a fine-grained methodology. To that end, we provide several studies for the best practices of the use of machine learning techniques for a scalable and efficient model. According to our experimental results and evaluations on obfuscators such as Tigress and OLLVM, our models have up to 91% accuracy on state-of-the-art obfuscation transformations. Our overall accuracies for their constructions are up to 100%
Model-based integration testing technique using formal finite state behavioral models for component-based software
Many issues and challenges could be identified when considering integration testing of Component-Based Software Systems (CBSS). Consequently, several research have appeared in the literature, aimed at facilitating the integration testing of CBSS. Unfortunately, they suffer from a number of drawbacks and limitations such as difficulty of understanding and describing the behavior of integrated components, lack of effective formalism for test information, difficulty of analyzing and validating the integrated components, and exposing the components implementation by providing semi-formal models. Hence, these problems have made it in effective to test todayās modern complex CBSS. To address these problems, a model-based approach such as Model-Based Testing (MBT) tends to be a suitable mechanism and could be a potential solution to be applied in the context of integration testing of CBSS. Accordingly, this thesis presents a model-based integration testing technique for CBSS. Firstly, a method to extract the formal finite state behavioral models of integrated software components using Mealy machine models was developed. The extracted formal models were used to detect faulty interactions (integration bugs) or compositional problems between integrated components in the system. Based on the experimental results, the proposed method had significant impact in reducing the number of output queries required to extract the formal models of integrated software components and its performance was 50% better compared to the existing methods. Secondly, based on the extracted formal models, an effective model-based integration testing technique (MITT) for CBSS was developed. Finally, the effectiveness of the MITT was demonstrated by employing it in the air gourmet and elevator case studies, using three evaluation parameters. The experimental results showed that the MITT was effective and outperformed Shahbaz technique on the air gourmet and elevator case studies. In terms of learned components for air gourmet and elevator case studies respectively, the MITT results were better by 98.14% and 100%, output queries based on performance were 42.13% and 25.01%, and error detection capabilities were 70.62% and 75% for each of the case study
- ā¦