Class management for software communities
Communications of the ACM
Quasi-random sequences and their discrepancies
SIAM Journal on Scientific Computing
Defining and Validating Measures for Object-Based High-Level Design
IEEE Transactions on Software Engineering
Experimentation in software engineering: an introduction
Experimentation in software engineering: an introduction
Applying UML and Patterns: An Introduction to Object-Oriented Analysis and Design and the Unified Process
Structured Design: Fundamentals of a Discipline of Computer Program and Systems Design
Structured Design: Fundamentals of a Discipline of Computer Program and Systems Design
Automatic Clustering of Software Systems Using a Genetic Algorithm
STEP '99 Proceedings of the Software Technology and Engineering Practice
Bunch: A Clustering Tool for the Recovery and Maintenance of Software System Structures
ICSM '99 Proceedings of the IEEE International Conference on Software Maintenance
A Multiple Hill Climbing Approach to Software Module Clustering
ICSM '03 Proceedings of the International Conference on Software Maintenance
An empirical study of the robustness of two module clustering fitness functions
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Genetic algorithms using low-discrepancy sequences
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Hybrid Evolutionary Algorithm for Solving Global Optimization Problems
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Quasi-random initial population for genetic algorithms
Computers & Mathematics with Applications
Software Module Clustering as a Multi-Objective Search Problem
IEEE Transactions on Software Engineering
Proceedings of the 33rd International Conference on Software Engineering
Variable grouping in multivariate time series via correlation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An analysis of the effects of composite objectives in multiobjective software module clustering
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Random number generators are a core component of heuristic search algorithms. They are used to build candidate solutions and to reduce bias while transforming these solutions during the search. Despite of their usefulness, random numbers also have drawbacks, as one cannot guarantee that all portions of the search space are covered and must run an algorithm many times to statistically evaluate its behavior. In this paper we present a study in which a Hill Climbing search with random restart was applied to the software clustering problem under two configurations. First, the algorithm used pseudo-random numbers to create the initial and restart solutions. Then, the algorithm was executed again but the initial and restart solutions were built according to a quasi-random sequence. Contrary to previous findings with other heuristic algorithms, we observed that the quasi-random search could not outperform the pseudo-random search for two distinct fitness functions and fourteen instances.