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Mixed-Integer Linear Programming Formulations for the Software Clustering Problem

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  • Viviane Köhler
  • Marcia Fampa
  • Olinto Araújo

Abstract

The clustering problem has an important application in software engineering, which usually deals with large software systems with complex structures. To facilitate the work of software maintainers, components of the system are divided into groups in such a way that the groups formed contain highly-interdependent modules and the independent modules are placed in different groups. The measure used to analyze the quality of the system partition is called Modularization Quality (MQ). Designers represent the software system as a graph where modules are represented by nodes and relationships between modules are represented by edges. This graph is referred in the literature as Module Dependency Graph (MDG). The Software Clustering Problem (SCP) consists in finding the partition of the MDG that maximizes the MQ. In this paper we present three new mathematical programming formulations for the SCP. Firstly, we formulate the SCP as a sum of linear fractional functions problem and then we apply two different linearization procedures to reformulate the problem as Mixed-Integer Linear Programming (MILP) problems. We discuss a preprocessing technique that reduces the size of the original problem and develop valid inequalities that have been shown to be very effective in tightening the formulations. We present numerical results that compare the formulations proposed and compare our results with the solutions obtained by the exhaustive algorithm supported by the freely available Bunch clustering tool, for benchmark problems. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Viviane Köhler & Marcia Fampa & Olinto Araújo, 2013. "Mixed-Integer Linear Programming Formulations for the Software Clustering Problem," Computational Optimization and Applications, Springer, vol. 55(1), pages 113-135, May.
    • Handle: RePEc:spr:coopap:v:55:y:2013:i:1:p:113-135
    DOI: 10.1007/s10589-012-9512-9
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    References listed on IDEAS

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    1. Li, Han-Lin, 1994. "A global approach for general 0-1 fractional programming," European Journal of Operational Research, Elsevier, vol. 73(3), pages 590-596, March.
    2. Danny Z. Chen & Ovidiu Daescu & Yang Dai & Naoki Katoh & Xiaodong Wu & Jinhui Xu, 2005. "Efficient Algorithms and Implementations for Optimizing the Sum of Linear Fractional Functions, with Applications," Journal of Combinatorial Optimization, Springer, vol. 9(1), pages 69-90, February.
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    Cited by:

    1. Hugo Harry Kramer & Eduardo Uchoa & Marcia Fampa & Viviane Köhler & François Vanderbeck, 2016. "Column generation approaches for the software clustering problem," Computational Optimization and Applications, Springer, vol. 64(3), pages 843-864, July.
    2. Lifeng Mu & Vijayan Sugumaran & Fangyuan Wang, 0. "A Hybrid Genetic Algorithm for Software Architecture Re-Modularization," Information Systems Frontiers, Springer, vol. 0, pages 1-29.
    3. Lifeng Mu & Vijayan Sugumaran & Fangyuan Wang, 2020. "A Hybrid Genetic Algorithm for Software Architecture Re-Modularization," Information Systems Frontiers, Springer, vol. 22(5), pages 1133-1161, October.

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