Awny Alnusair

Component search and reuse: An ontology-based approach

In order to realize the full potential of software reuse, effective search techniques are indeed essential. In this paper, we propose a semantic-based approach for retrieving relevant components from a reuse repository. This approach utilizes an ontology model that includes a source-code ontology, a component ontology, and a domain-specific ontology. Due to the indexing and knowledge population mechanisms we used, our approach supports various kinds of search techniques. However, our experiments show evidence that only pure semantic search that exploits domain knowledge tends to improve precision.

Effective API navigation and reuse

Most reuse libraries come with few source-code examples that demonstrate how the library at hand should be used. We have developed a source-code recommendation approach for constructing and delivering relevant code snippets that programmers can use to complete a certain programming task. Our approach is semantic-based; relying on an explicit ontological representation of source-code. We argue that such representation opens new doors for an improved recommendation mechanism that ensures relevancy and accuracy. Current recommendation systems require an existing repository of relevant code samples. However, for many libraries, such a repository does not exist. Therefore, we instead utilize points-to analysis to infer precise type information of library components. We have backed our approach with a tool that has been tested on multiple libraries. The obtained results are promising and demonstrate the effectiveness of our approach.

Rule-based detection of design patterns in program code

The process of understanding and reusing software is often time-consuming, especially in legacy code and open-source libraries. While some core code of open-source libraries may be well-documented, it is frequently the case that open-source libraries lack informative API documentation and reliable design information. As a result, the source code itself is often the sole reliable source of information for program understanding activities. In this article, we propose a reverse-engineering approach that can provide assistance during the process of understanding software through the automatic recovery of hidden design patterns in software libraries. Specifically, we use ontology formalism to represent the conceptual knowledge of the source code and semantic rules to capture the structures and behaviors of the design patterns in the libraries. Several software libraries were examined with this approach and the evaluation results show that effective and flexible detection of design patterns can be achieved without using hard-coded heuristics.

Retrieving Reusable Software Components Using Enhanced Representation of Domain Knowledge

This paper describes an ontology-based approach for identifying and retrieving relevant software components in large reuse libraries. Since it is usually difficult to precisely identify exact matches without considering domain knowledge, we exploit the use of domain-specific ontologies to enrich a knowledge base initially populated with multi-faceted ontological descriptions of API components. In addition to pure semantic-based search, this enriched knowledge base supports signature-based search, keyword search, and blended search. However, our experiments show evidence that only semantic search that is backed by reasoning services enables intelligent matchmaking and yields improved precision. Based on a usability case study, we further argue that semantic search is indeed usable and practical.

Using ontology reasoning for reverse engineering design patterns

Capturing design knowledge in large software systems reduces the complexity of understanding and reusing these systems. Model Driven Engineering (MDE) is seen by many as the new trend to cope with software complexity. MDE promotes the notion of modeling and model transformations in model-driven development. In this paper, we propose an approach that utilizes ontological modeling and reasoning for recovering design pattern information from source code. We thus provide a formal representation of the conceptual knowledge found in source code and match it to similar representation of design patterns. This proper matching is the basis for applying semantic reasoning to infer design pattern instances. We have tested our approach on multiple open source application frameworks. The results we obtained are promising and show an improvement in terms of recall and precision.

Tag-based personalized recommendation in social media services

Users of ambient intelligence environments have been overwhelmed by the huge numbers of social media available, thus identifying the social media tailored to the user’s need is becoming an important question to be discussed. This paper adapts the Katz proximity measure, for the use in social tagging system, to help users in ambient environment find relevant media suited to their interests. The method models the ternary relations among user, resource and tag as a weighted, undirected tripartite graph, then apply the Katz proximity measure to tripartite graph. Experiments on two real datasets are implemented and compared with many state-of-the-art algorithms. The experimental results prove that the adaptation of the Katz algorithm with the tripartite structure yields a significant improvement, and successfully ranks relevant search results according to the user’s interests.

Graph-based personalized recommendation in social tagging systems

In recent years, users of ambient intelligence environments have been overwhelmed by the huge numbers of social media available. Consequentially, users have trouble finding social media suited to their needs. To help users in ambient environment get relevant media tailored to their interests, we propose a new method which adapts the Katz measure, a path-ensemble based proximity measure, for the use in social tagging services. We model the ternary relations among user, resource and tag as a weighted, undirected tripartite graph. We then apply the Katz measure to this graph, and exploit it to provide personalized recommendation for individual users within ambient intelligence environments. The experimental evaluations show that the proposed method improves the recommendation performance compared to existing algorithms.

Context-aware multimodal recommendations of multimedia data in cyber situational awareness

The current proliferation of large amounts of multimedia data creates an unprecedented challenge for security analysts in the context of Cyber Situational Awareness. Due to this phenomenal growth of multimedia data, security analysts have to invest enormous time and efforts in filtering and correlating multimedia data in order to make informed decisions about identifying and mitigating threats and vulnerabilities. In particular, analysts have to analyze and interpret diverse multimedia network data with varying contexts in order to find the true evidence of cyber attacks. Considering the multimedia nature of cyber security data, we propose a cloud-assisted recommendation system that can identify and retrieve multimedia data of interest based on contextual information and security analysts’ personal preferences. This recommendation system benefits security analysts by establishing a bridge between their personal preferences, the contextual information of their analytical process, and the various types of modality of multimedia data. Evaluation of the proposed system shows evidence that our multimedia recommendation mechanisms promotes cyber threat understanding and risk assessment.

Mining vehicular data in VANET

Vehicular networks can generate a large amount of vehicular data which are useful to both car consumers and manufacturers. In this paper we propose to collect vehicular data and to mine vehicular data by using data mining models. The experiment results shows the proposed methods are effective.

Automatic recognition of design motifs using semantic conditions

Program comprehension is vital for building, enhancing and maintaining existing software systems. In this paper, we propose an automatic reverse engineering approach that leverages understanding and reusing software libraries through the automatic recovery of the motifs described by design patterns. Initially we formalise the description of common patterns. We then exploit Semantic Web knowledge representation mechanisms for capturing these descriptions in source-code. Empirical evaluations of this approach show evidence that when conceptual knowledge of source-code is represented using ontology formalisms, and when semantic rules are used to capture pattern structure and behavior, we can achieve an effective and flexible detection of patterns without relying on hard-coded heuristics.