The video-game industry has recently grown from focused markets to mainstream. The advancements the industry has been enjoying motivated researchers to propose techniques and tools to support the activities across the different phases of the game development lifecycle. Game testing is one of the crucial activities within the game development process. Due to the nature of game testing, many automated game testing techniques have been proposed in the literature. However, there is no framework that could be used to aid practitioners in selecting appropriate techniques suitable for their particular development efforts. In this article, we present an attribute-based framework to classify and compare these techniques and provide such aid to practitioners. The framework is also meant to guide researchers interested in proposing new game testing techniques. The article discusses a number of prominent techniques against the framework. Analysis of the discussion reveals gaps and suggests open points for future research.

BACKGROUND: Protein-protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular structure of such biological networks. Although clustering techniques have been proposed for clustering protein-protein interaction networks, those techniques suffer from some drawbacks. The application of earlier clustering techniques to protein-protein interaction networks in order to predict protein complexes within the networks does not yield good results due to the small-world and power-law properties of these networks. RESULTS: In this paper, we construct a new clustering algorithm for predicting protein complexes through the use of genetic algorithms. We design an objective function for exclusive clustering and overlapping clustering. We assess the quality of our proposed clustering algorithm using two gold-standard data sets. CONCLUSIONS: Our algorithm can identify protein complexes that are significantly enriched in the gold-standard data sets. Furthermore, our method surpasses three competing methods: MCL, ClusterOne, and MCODE in terms of the quality of the predicted complexes. The source code and accompanying examples are freely available at http://faculty.kfupm.edu.sa/ics/eramadan/GACluster.zip .

Systematic software reuse facilitates achieving quality software, faster and at a lower cost. Software reuse environments play an important role in exploiting reusable artifacts to the extent. Environments that support reusing early-stage software artifacts are of great interest. There have been a number of reuse environments proposed in the literature for reuse of software artifacts. However, there is no framework to allow analyzing (i.e. classifying and comparing) reuse environments. The significance of such a framework is twofold: (1) to be used by reuse practitioners to select appropriate environment; and (2) to be used by researchers to identify gaps for future research. In this paper, we present an attribute-based framework to provide such aid to practitioners and researchers. The paper also presents an indicative survey of current reuse environments proposed for systematic reuse of software artifacts. We use the framework to analyze those reuse environments. The analysis provides an understanding of current systematic reuse approaches and respective reuse environments; and identifies corresponding shortcomings. Consequently, we post research questions for future work.

Reuse of software results in many gains such as reduced development time and overall cost, especially when it takes place in the early stages of software development. Retrieval is a crucial activity during software reuse. This work focuses on the retrieval of UML class diagrams using Genetic Algorithm (GA). It builds on our earlier work by describing a GA for determining class diagram similarity based on classifier features. Experimental results show that our newly proposed technique results in the retrieval of the most similar class diagrams from a repository.

Cohesion is an internal software attribute which depicts how well the components of a software module are connected. It is thought of as having effect on the quality of the software system. This paper presents the results of an empirical investigation of whether cohesion of object-oriented systems plays a role on software reliability. The paper presents a study of the correlation between cohesion measures and the number of defects in software systems, using seven open source projects.

There has been an emerging trend to develop software using different components. In this way the cost of the software reduces and the developer is able to complete the system efficiently. The components' code may or may not be visible to the developer. Testing, in this case, requires the development of a set of test configurations that can be applied on the software. However, for software that comprises a large number of components, it is infeasible to test each and every test configuration within the limited testing budget and time. In this paper we propose a GA-based technique that identifies a set of test configurations that are expected to maximize pair-wise coverage, with the constraint that the number of test configurations is predefined. Although the paper primarily focuses on the interaction between software components, the idea can be applied to single code component testing. We performed some experiments using our proposed approach. The results were promising.

Effective and efficient test data generation is one of the major challenging and time-consuming tasks within the software testing process. Researchers have proposed different methods to generate test data automatically, however, those methods suffer from different drawbacks. In this paper we present a genetic algorithm-based approach that tries to generate a test data that is expected to cover a given set of target paths. Our proposed fitness function is intended to achieve path coverage that incorporates path traversal techniques, neighborhood influence, weighting, and normalization. This integration improves the GA performance in terms of search space exploitation and exploration, and allows faster convergence. We performed some experiments using our proposed approach, where results were promising.

Command, control, communications, computers, and intelligence (C/sup 4/I) systems are used to assist organizations in achieving their respective goals and objectives. Although organizations are becoming increasingly more dependent on C/sup 4/I systems, there is a lack of automated tools for carrying out the planning functions inherent in C/sup 4/I systems. The paper presents a framework for agent based C/sup 4/I systems. The paper presents an approach and issues for developing such C/sup 4/I systems.

Information distribution rules (IDRs) can be utilized to effectively and efficiently control event-driven information flow between objects in distributed-object systems. The problem with IDRs is that as the configuration of the distributed system gets larger (i.e., large number of objects, large number of possible events, more need for information, large number of communication constraints, etc.), it is very difficult to generate and debug IDRs manually. Additionally, having faulty IDRs can lead to serious undesirable situations. This paper presents an algorithm to generate near-optimal IDRs for efficient and effective information flow. Illustrative examples as well as complexity analysis of the behaviour of this algorithm are included in the paper.