Implementing Rough Set Technique in Software Engineering Process for Information Extraction | Original Article

ABSTRACT:

Our paper deals on the topic of 'Intelligent Information Retrieval'. There are many processes for extracting knowledge form a complete information system. As observed, in many cases, real Jo problems are uncertain; Ire may categorize such problem under the incomplete information system. It is difficult to deal with those systems having knowledge having 'incompleteness' (some values in dataset is missing) and 'inconsistencies' (ambiguities and contradicting values in dataset) in nature. Hence, the process of knowledge and information extraction becomes more challenging where real time data are incomplete. Rough Set can be used as a tool for clustering and handling incomplete information. In this paper we have defined and discussed on some of the known properties of Rough Set and we have implemented the tool to generate information for a software engineering process. Planning for delivery and installation for any software requires', burning for procurement of hardware, software, and skilled manpower (software developers). The process of delivering the software also consists of preparing the documentation and manuals, and planning for training. Scheduling for delivery and installation within the deadline and estimated coast, on the other hand, requires the preparation of a time table for putting the system in place. It is desirable in many cases, that the new software is installed while the old system still operates, as the automation system need to be running. We have studied and analyzed our proposed technique for one such software. i.e. COA. COA (Control Office Application) runs for CIUS (Centre for Railway Information System) of Indian Railway. COA manly deals with the automation of Arrival, Departure and running of the trains. Earlier CRIS implemented and succeeded in another project i.e. FOIS (Freight Operation information System). This is one of the Asia's biggest networks in any organization. In our analysis, we observed that, selecting Evolutionary Model as SOW for up gradation and modification of this process may give a better result, it was also observed that, selection of appropriate skilled manpower software developers) is one of the key factors for success and in-time delivery, when SDLC-Evolutionary Model is followed. During the process of selection of appropriate skilled manpower, it was found that the dataset consist of some missing, incomplete and uncertain information. Hence, we proposed and used Rough Set as a tool for classification, chutes- and to generate knowledge, which can be used for software procurement planning.