Abstract-Object-oriented (OO) software is usually organized into subsystems using the concepts of package or module. Such modular structure helps applications to evolve when facing new requirements. However, studies show that as software evolves to meet requirements and environment changes, modularization quality degrades. To help maintainers improve the quality of software modularization we have designed and implemented a heuristic search-based approach for automatically optimizing inter-package connectivity (i.e., dependencies). In this paper, we present our approach and its underlying techniques and algorithm. We show through a case study how it enables maintainers to optimize OO package structure of source code. Our optimization approach is based on Simulated Annealing technique.
Abstract-Recently, there has been an important progress in applying search-based optimization techniques to the problem of software re-modularization. Yet, a major part of the existing body of work addresses the problem of modularizing software systems from scratch, regardless of the existing packages structure. This paper presents a novel multi-objective optimization approach for improving existing packages structure. The optimization approach aims at increasing the cohesion and reducing the coupling and cyclic connectivity of packages, by modifying as less as possible the existing packages organization. Moreover, maintainers can specify several constraints to guide the optimization process with regard to extra design factors. To this contribution, we use the Non-Dominated Sorting Genetic Algorithm (NSGA-II). We evaluate the optimization approach through an experiment covering four real-world software systems. The results promise the effectiveness of our optimization approach for improving existing packages structure by doing very small modifications.
Design space exploration (DSE) aims to find optimal design candidates of a domain with respect to different objectives where design candidates are constrained by complex structural and numerical restrictions. 14,18] aims to find such candidates that are reachable from an initial model by applying a sequence of exploration rules. Solving a rule-based DSE problem is a difficult challenge due to the inherently dynamic nature of the problem.In the current paper, we propose to integrate multi-objective optimization techniques by using Non-dominated Sorting Genetic Algorithms (NSGA) to drive rule-based design space exploration. For this purpose, finite populations of the most promising design candidates are maintained wrt. different optimization criteria. In our context, individuals of a generation are defined as a sequence of rule applications leading from an initial model to a candidate model. Populations evolve by mutation and crossover operations which manipulate (change, extend or combine) rule execution sequences to yield new individuals.Our multi-objective optimization approach for rule-based DSE is domain independent and it is automated by tooling built on the Eclipse framework. The main added value is to seamlessly lift multi-objective optimization techniques to the exploration process preserving both domain independence and a high-level of abstraction. Design candidates will still be represented as models and the evolution of these models as rule execution sequences. Constraints are captured by model queries while objectives can be derived both from models or rule applications. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.
There exist many large object-oriented software systems consisting of several thousands of classes that are organized into several hundreds of packages. In such software systems, classes cannot be considered as units for software modularization. In such context, packages are not simply classes containers, but they also play the role of modules: a package should focus to provide well identified services to the rest of the software system. Therefore, understanding and assessing package organization is primordial for software maintenance tasks. Although there exist a lot of works proposing metrics for the quality of a single class and/or the quality of inter-class relationships, there exist few works dealing with some aspects for the quality of package organization and relationship. We believe that additional investigations are required for assessing package modularity aspects. The goal of this paper is to provide a complementary set of metrics that assess some modularity principles for packages in large legacy object-oriented software: Information-Hiding, Changeability and Reusability principles. Our metrics are defined with respect to object-oriented inter-package and intra-package dependencies. The dependencies that are caused by different types of inter-class dependencies, such as inheritance and method call. We validate our metrics theoretically through a careful study of the mathematical properties of each metric.
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