Methodology for Validating Software Metrics
IEEE Transactions on Software Engineering
A Pattern Recognition Approach for Software Engineering Data Analysis
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
IEEE Transactions on Software Engineering - Special issue on software reliability
Rule-based fuzzy classification for software quality control
Fuzzy Sets and Systems - Special issue on industrial applications
Experimental design and analysis in software engineering, part 5: analyzing the data
ACM SIGSOFT Software Engineering Notes
Communications of the ACM
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Software Engineering Economics
Software Engineering Economics
Soft Computing and Fuzzy Logic
IEEE Software
Proceedings of the International Workshop on Experimental Software Engineering Issues: Critical Assessment and Future Directions
A comparative study of attribute weighting heuristics for effort estimation by analogy
Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering
An empirical study of using rule induction and rough sets to software cost estimation
Fundamenta Informaticae - Special issue on theory and applications of soft computing (TASC04)
Review: Dimensionality reduction based on rough set theory: A review
Applied Soft Computing
Does explanation improve the acceptance of decision support for product release planning?
ESEM '09 Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement
Two machine-learning techniques for mining solutions of the ReleasePlannerTM decision support system
Information Sciences: an International Journal
| Hi-index | 0.01 |
Analysis of Software Engineering data is often concerned with treatment of incomplete knowledge, with management of inconsistent pieces of information and with manipulation of various levels of representation of data. Existing techniques of data analysis are mainly based on quite strong assumptions (some knowledge about dependencies, probability distributions, large number of experiments), are unable to derive conclusions from incomplete knowledge, or can not manage inconsistent pieces of information. A rough set is a collection of objects which, in general, cannot be precisely characterized in terms of the values of the set of attributes, while a lower and an upper approximation of the collection can do. Rough sets were successfully applied for data analysis in different areas. In this paper, the approach is applied for analysis of Software Engineering data resulting from goal-oriented measurement. Fundamental principles and concepts of rough sets are presented. They are illustrated by the example to predict criticality of software modules based on metrics data from early development phases. In a further application, analysis of COCOMO cost drivers is studied.