International Journal of Software Engineering and Knowledge Engineering

Reducing the variance between expectation and execution of software processes is an essential activity for software development, in which the Causal Analysis is a conventional means of detecting problems in the software process. However, significant effort may be required to identify the problems of software development. Defect prevention prevents the problems from occurring, thus lowering the effort required in defect detection and correction. The prediction model is a conventional means of predicting the problems of subsequent process actions, where the prediction model can be built from the performed actions. This study proposes a novel approach that applies the Intertransaction Association Rule Mining techniques to the records of performed actions in order to discover the patterns that are likely to cause high severity defects. The discovered patterns can then be applied to predict the subsequent actions that may result in high severity defects.

Simulation of complex Lotos specifications is not always efficient due to the space explosion problem of their corresponding transition systems. To overcome this difficulty in practice, we present in this paper a novel approach which integrates constraint propagation techniques into the Lotos specifications. These solving techniques are used to reduce the size of the search space before and during the search for a solution to a given combinatorial problem under constraints. In order to do that, we first tackle the challenging task of describing combinatorial problems in Lotos using the Constraint Satisfaction Problem (CSP) framework. In this regard, we provide two generic Lotos templates for describing CSPs and temporal CSPs (CSPs involving temporal constraints). To evaluate the time performance of the framework we propose, we have conducted several experimental tests on instances of the N-Queens, the machine scheduling and randomly generated CSPs. The results of these experiments are promising and demonstrate the efficiency of Lotos simulation when CSP techniques are integrated.

Services as abstractions of functionality have enabled the engineering of systems that support well-defined processes with relative ease. This success leads to aspirations for achieving greater complexity with the service-oriented paradigm. In particular, we address the case where the process definition is tailored differently in each instantiation based on negotiations among stakeholders of a socio-technical context. For such cases the process definition invariably crosscuts the architecture of a process-support system that composes available services. However, use of pre-defined process variations may bias the tailoring effort and thus, act against the original motivation of having a flexible definition. On the other hand, the characteristics of process complexity and tailorability introduce differences between stakeholder understanding of the process activities and their manifestation in tool support. We encounter these issues while developing a service-oriented process-support system for a security Certification and Accreditation (C&A) process. In this paper, we present our approach to effectively separate the C&A process definition from the architecture of its process-support system. We employ ontological modeling techniques to explicitly model the process definition and later expose it as a service to provide weaving rules for dynamically composing the process-support system architecture at runtime. The feasibility of our approach has been demonstrated in the design of a service-oriented architecture for a prototype workbench that supports the Department of Defense Certification and Accreditation Process (DITSCAP).

The quality of a software system highly depends on its architectural design. High quality software systems typically apply expert design experience which has been captured as design patterns. As demonstrated solutions to recurring problems, design patterns help to reuse expert experience in software system design. They have been extensively applied in the industry. Mining the instances of design patterns from the source code of software systems can assist in the understanding of the systems and the process of re-engineering them. More importantly, it also helps to trace back to the original design decisions, which are typically missing in legacy systems. This paper presents a review on current techniques and tools for mining design patterns from source code or design of software systems. We classify different approaches and analyze their results in a comparative study. We also examine the disparity of the discovery results of different approaches and analyze possible reasons with some insight.

Graph Grammars have recently become more and more popular as a general formal modeling language. Behavioral modeling of dynamic systems and model to model transformations are a few well-known examples in which graphs have proven their usefulness in software engineering. A special type of graph transformation systems is layered graphs. Layered graphs are a suitable formalism for modeling hierarchical systems. However, most of the research so far concentrated on graph transformation systems as a modeling means, without considering the need for suitable analysis tools.

In this paper we concentrate on how to analyze these models. We will describe our approach to show how one can verify the designed graph transformation systems. To verify graph transformation systems we use a novel approach: using Bogor model checker to verify graph transformation systems. The AGG-like graph transformation systems are translated to BIR — the input language of Bogor — and Bogor verifies that model against some properties defined by combining LTL and special purpose graph rules. Supporting schema-based and layered graphs characterize our approach among existing solutions for verification of graph transformation systems.

Multiple sequence alignment (MSA) has become an important issue in computational molecular biology. The purpose of MSA is to infer evolutionary history or discover homologous regions among closely related DNA or protein sequences. A wide variety of approaches has been proposed for the MSA problem; however, some of them need prerequisites to find the best alignment or may suffer from the drawbacks of high computational complexity and huge memory requirement so they can be only applied to cases with a limited number of sequences. In this paper, we view the MSA problem as an optimization problem and resolve it by applying a genetic algorithm. In order to improve the search performance and obtain a better alignment quality, we design a few problem-specific genetic operators. Experimental results on real DNA and protein sequences are given to illustrate the feasibility of the proposed approach.