International Journal of Software Engineering and Knowledge Engineering

Despite the presence of many systems for developing and managing structured taxonomies and/or SKOS models for a given domain for which small documents set are accessible, the production and maintenance of these domain knowledge bases is still a very expensive and time consuming process. This paper proposes a solution for assisting expert users in the development and management of knowledge base, including SKOS and ontologies modeling structures and relationships. The proposed solution accelerates the knowledge production by crawling and exploiting different kinds of sources (in multiple languages and with several inconsistencies among them). The proposed tool supports the experts in defining relationships among the most recurrent concepts, reducing the time to SKOS production and allowing assisted production. The validity of the produced knowledge base has been assessed by using SPARQL query interface and a precision and recall model. The results have demonstrated better performance with respect to the state of the art. The solution has been developed for Open Space Innovative Mind project, with the aim of creating a portal to allow industries at posing semantic queries to discover potential competences in a large institution such as the University of Florence, in which several distinct domains are associated with its own departments.

Chang [S. K. Chang, A general framework for slow intelligence systems, International Journal of Software Engineering and Knowledge Engineering, 20(1) (2010) 1–18.] argues that slow intelligence is a property shared by a large number of intelligent systems, and proposes a general framework, which has quick/slow decision cycles. The aim of this paper is to show that the general framework is applicable for modeling human intelligence. We show that the quick/slow decision cycles model the procedural/declarative memory system, and collect supports from adults' second language acquisition and aphasic bilinguals. As spatial cognition is fundamental in human intelligence, we show in detail how object tracing can be simulated within slow intelligence framework. We introduce a psychological experiment on object tracing, and present two rules in psychology which govern object tracing results under certain conditions, namely, the rule of minimal spatial transformation, and the rule of categorical continuity. We review related works in spatial knowledge representation and reduce object tracing problems into the problem of object mapping. We show that the following capabilities can be simulated in the slow intelligence framework: the capability of acquiring knowledge about one scene, and the capability of mapping object between scenes. An experimental result is presented.

Knowledge-based systems (KBSs) or expert systems (ESs) are able to solve problems generally through the application of knowledge representing a domain and a set of inference rules. In knowledge engineering (KE), the use of KBSs in the real world, three principal disadvantages have been encountered. First, the knowledge acquisition process has a very high cost in terms of money and time. Second, processing information provided by experts is often difficult and tedious. Third, the establishment of mark times associated with each project phase is difficult due to the complexity described in the previous two points. In response to these obstacles, many methodologies have been developed, most of which include a tool to support the application of the given methodology. Nevertheless, there are advantages and disadvantages inherent in KE methodologies, as well. For instance, particular phases or components of certain methodologies seem to be better equipped than others to respond to a given problem. However, since KE tools currently available support just one methodology the joint use of these phases or components from different methodologies for the solution of a particular problem is hindered. This paper presents KEManager, a generic meta-tool that facilitates the definition and combined application of phases or components from different methodologies. Although other methodologies could be defined and combined in the KEManager, this paper focuses on the combination of two well-known KE methodologies, CommonKADS and IDEAL, together with the most commonly-applied knowledge acquisition methods. The result is an example of the ad hoc creation of a new methodology from pre-existing methodologies, allowing for the adaptation of the KE process to an organization or domain-specific characteristics. The tool was evaluated by students at Carlos III University of Madrid (Spain).

Test Driven Development (TDD), as a quality promotion approach, suffers from some shortages that discourage its usage. One of the most challenging shortcomings of TDD is the low level of granularity and abstraction. This may lead to production of software that is not acceptable by the end users. Additionally, exploiting of TDD is not applicable in the enterprise systems development. To overcome this defect, we have merged TDD with Model Based Testing (MBT) and suggested a framework named Model Based Test Driven Development (MBTDD). According to TDD, writing test cases comes before programming, and based on our improved method of TDD, modeling precedes writing test cases. To validate the applicability of the proposed framework, we have implemented a use case of Human Resource Management (HRM) system by means of MBTDD. The empirical results of using MBTTD show that our proposed method overwhelms existing deficiencies of TDD.

With increasing demands on software functions, software systems become more and more complex. This complexity is one of the most pervasive factors affecting software development productivity. Assessing the impact of software complexity on development productivity helps to provide effective strategies for development process and project management. Previous research literatures have suggested that development productivity declines exponentially with software complexity. Borrowing insights from cognitive learning psychology and behavior theory, the relationship between software complexity and development productivity was reexamined in this paper. This research identified that the relationship partially showed a U-shaped as well as an inverted U-shaped curvilinear tendency. Furthermore, the range of complexity level that is beneficial for productivity has been presented, in which, the lower bound denotes the minimum degree of complexity at which personnel can be motivated, while the upper bound shows the maximum extent of complexity that staff can endure. Based on our findings, some guidelines for improving personnel management of software industry have also been given.

Evolutionary structural testing has been researched and promising results have been presented. However, it has hardly been applied to real-world complex systems and as such, little is known about the scalability, applicability and acceptability of it in an industrial setting. The European project EvoTest (IST-33472) team has been working from 2006 till 2009 to improve this situation and this paper informs about the results. We start with an overview of tools and techniques which we have developed for automated evolutionary structural testing. Subsequently, we describe the empirical setup used to study the applicability of evolutionary structural testing in industry through two case studies. The test objects used for the studies are selected functions (handwritten and generated) from production systems at Daimler and Berner & Mattner Systemtechnik (BMS) like, for example, Rear Window Defroster, Global Powertrain Engine Controller, Window Lift Control System, etc. The results of the case studies are described and research questions are assessed based on the obtained results. In summary, the results indicate that evolutionary structural testing in an industrial setting is worthwhile and profitable. Hardly any detailed knowledge of evolutionary computation is required to search for interesting test data. The case studies also research the benefits of using techniques like automated parameter tuning and search space smoothing.