International Journal of Software Engineering and Knowledge Engineering

Rough set based methods have been applied successfully in many real world applications such as data mining, knowledge discovery, machine learning, and control. The rough set theory is used to deal with imperfect data and to eliminate dispensable, superfluous and redundant information as to obtain a simplified set of decision rules. Thus, several approaches and methods have been proposed to find minimal coverings, from which the decision rules can be induced. In many of these approaches, an improvement in the utilization of computational resources is encouraged.

In this paper, a binary encoding for attribute sets and a discernibility matrix is proposed. Such a binary representation of sets and sets operations in the implementation of algorithms provides a machine-oriented approach to the utilization of computational memory and allow parallel processing among groups of attributes. The discernibility matrix is reduced to its minimal size through the identification of main patterns in order to eliminate redundancies. Bit-wise operations replace sets operations, thus the search for minimal coverings is performed in an efficient way. Resulting improvement is shown in the analysis of medium-sized data sets using two generic methods to obtain minimal coverings.

A use case represents a unit of the functionality specification of a system. Industrial object-oriented projects have applied use cases to capture user requirements. Use cases can be used throughout the whole process of object-oriented software development [14]. But, the problem of how to write use cases is still puzzling even software experts [6]. It reflects the lack of a systematic approach to capturing use cases. The set of use cases in a use case model is unstructured regarding the structure of the problem addressed by an application. This paper presents a notion of a use case pattern and proposes using a use case pattern to guide use case capturing. A use case pattern encodes reusable knowledge on the structure and function of a specific class of applications. It guides the work of use case gathering. We illustrate the approach to use case modeling with several use case patterns.

In current literature few detailed process models for Requirements Engineering are presented: usually high-level activities are distinguished, without a more precise specification of each activity. In this paper the process of Requirements Engineering has been analyzed using knowledge-level modelling techniques, resulting in a well-specified compositional process model for the Requirements Engineering task. This process model is considered to be a generic process model: it can be refined (by instantiation or specialisation) into a process model for a specific kind of Requirements Engineering process.

Modernizing heavily evolved and poorly documented information systems is a central software engineering problem in our current IT industry. It is often necessary to reverse engineer the design documentation of such legacy systems. Several interactive CASE tools have been developed to support this human-intensive process. However, practical experience indicates that their applicability is limited because they do not adequately handle imperfect knowledge about legacy systems. In this paper, we investigate the applicability of several major theories of imperfect knowledge management in the area of soft computing and approximate reasoning. The theories are evaluated with respect to how well they meet requirements for generating effective human-centred reverse engineering environments. The requirements were elicited with help from practical case studies in the area of database reverse engineering. A particular theory called "possibilistic logic" was found to best meet these requirements most comprehensively. This evaluation highlights important challenges to the designers of knowledge management techniques, and should help reverse engineering tool implementers select appropriate technologies.