International Journal of Software Engineering and Knowledge Engineering

Along with the fast development in information and communication technology (ICT), job skills required by ICT industries are also evolving very rapidly. It becomes difficult for ICT students to assess the gap between their skills and such evolving skills. Even though schools perform periodical curriculum evaluations, the time gap between the evaluations causes the curriculum to get out-of-date easily since it is unable to cope with the tremendous and quick changes occurring in the industry. We propose novel solutions by introducing some measures and visualization tools to reveal such skills’ gap. Using evolutionary-based data mining, the skillsets mastered by students were collected from their study reports, while the frequent skillsets required by the industry were mined out from job adverts; and based on these skillsets the skill coverage of the students was approximated. The proposed solutions were then tested on data obtained from an Indonesian higher education institution since Indonesia implements competence-based curriculum in its education system. Experimental works show that the proposed approaches not only reveal and visualize the gap, but also monitor the changes in the skills requirements, which also help the school’s administrator while updating the curriculum.

Application frameworks are a powerful technique for large-scale reuse, but require a considerable effort to understand them. Good documentation is costly, as it needs to address different audiences with disparate learning needs. When code and documentation prove insufficient, developers turn to their network of experts. Nevertheless, this proves difficult, mainly due to the lack of expertise awareness (who to ask), wasteful interruptions of the wrong people and unavailability (either due to intrusion or time constraints). The DRIVER platform is a collaborative learning environment where framework users can, in a non-intrusive way, store and share their learning knowledge while following the best practices of framework understanding (patterns). Developed by the authors, it provides a framework documentation repository, mounted on a wiki, where the learning paths of the community of learners can be captured, shared, rated, and recommended. Combining these social activities, the DRIVER platform promotes collaborative learning, mitigating intrusiveness, unavailability of experts and loss of tacit knowledge. This paper presents the assessment of DRIVER using a controlled academic experiment that measured the performance, effectiveness and framework knowledge intake of MSc students. The study concluded that, especially for novice learners, the platform allows for a faster and more effective learning process.

Currently, tacit knowledge has attracted increasing research attention. However, the theoretical foundation of tacit knowledge is still not well formulated, because the researches are very disperse. This work provides a review of the current researches. First, the definition of tacit knowledge is discussed by answering several questions. Next, tacit knowledge sharing, tacit knowledge quantization are identified as two research topics in the current research community. Following that, the technical progress of each topic is summarized and analyzed. Finally, we provide a thumbnail of the researches and identify three research consensuses to answer where we are. While, seven research directions are identified to answer where we shall go.

This paper aims at investigating empirically the effect of aspect-oriented (AO) refactoring on the unit testability of classes in object-oriented software. The unit testability of classes has been addressed from the perspective of the unit testing effort, and particularly from the perspective of the unit test cases (TCs) construction. We investigated, in fact, different research questions: (1) the impact of AO refactoring on source code attributes (size, complexity, coupling, cohesion and inheritance), attributes that are mostly related to the unit testability of classes, (2) the impact of AO refactoring on unit test code attributes (size, assertions, invocations and data creation), attributes that are indicators of the effort involved to write the code of unit TCs, and (3) the relationships between the variations observed after AO refactoring in both source code and unit test code attributes. We used in the study different techniques: correlation analysis, statistical tests and linear regression. We performed an empirical evaluation using data collected from three well-known open source (Java) software systems (JHOTDRAW, HSQLBD and PETSTORE) that have been refactored using AO programming (AspectJ). Results suggest that: (1) overall, the effort involved in the construction of unit TCs of refactored classes has been reduced, (2) the variations of source code attributes have more impact on methods invocation between unit TCs, and finally (3) the variations of unit test code attributes are more influenced by the variation of the complexity of refactored classes compared to the other class attributes.

Nowadays, information/data security and availability are of utmost importance. However, due to the fact that security is a process rather than a state, there is an increasing demand for technologies or architectural solutions that would allow a computer system to adjust its level of security in response to changes in its environmental/network characteristics. In this paper, an architecture for a self-managing adaptive router/firewall has been proposed to facilitate an intelligent and real-time self-protection of a computer system. We also show how the proposed architecture might be used to control other system mechanisms or resources (for example, RAM).

The Feature Model (FM) is a fundamental artifact of the Software Product Line (SPL) engineering, used to represent commonalities and variabilities, and also to derive products for testing. However, the test of all features combinations (products) is not always possible in practice. Due to the growing complexity of the applications, only a subset of products is usually selected. The selection is generally based on combinatorial testing, to test features interactions. This kind of selection does not consider different classes of faults that can be present in the FM. The application of a fault-based approach, such as mutation-based testing, can increase the probability of finding faults and the confidence that the SPL products match the requirements. Considering that, this paper introduces a mutation approach to select products for the feature testing of SPLs. The approach can be used similarly to a test criterion in the generation and assessment of test cases. It includes (i) a set of mutation operators, introduced to describe typical faults associated to the feature management and to the FM; and (ii) a testing process to apply the operators. Experimental results show the applicability of the approach. The selected test case sets are capable to reveal other kind of faults, not revealed in the pairwise testing.

Twitter is a microblogging website, which has different characteristics from any other social networking service (SNS) in that it has one-directional relationships between users with short posts of less than 140 characters. These characteristics make Twitter not only a social network but also a news media. In addition, Twitter posts have been used and analyzed in various fields such as marketing, prediction of presidential elections, and requirement analysis. With an increase in Twitter usage, we need a more effective method to analyze Twitter content. In this paper, we propose a method for content analysis based on the influence of Twitter content. For measuring Twitter influence, we use the number of followers of the content author, retweet count, and currency of time. We perform experiments to compare the proposed method, frequency, numerical statistics, user influence, and sentiment score. The results show that the proposed method is slightly better than the other methods. In addition, we discuss Twitter characteristics and a method for an effective analysis of Twitter content.