The imperfect hiding : some introductory concepts and preliminary issues on modularity

In this work we present a critical assessment of some problems and open questions on the debated notion of modularity. Modularity is greatly in fashion nowadays, being often proposed as the new approach to complex artefact production that enables to combine fast innovation pace, enhanced product variety and reduced need for co-ordination. In line with recent critical assessments of the managerial literature on modularity, we sustain that modularity is only one among several arrangements to cope with the complexity inherent in most high-technology artefact production, and by no means the best one. We first discuss relations between modularity and the broader (and much older within economics) notion of division of labour. Then we sustain that a modular approach to labour division aimed at eliminating technological interdependencies between components or phases of a complex production process may have, as a by-product, the creation of other types of interdependencies which may subsequently result in inefficiencies of various types. Hence, the choice of a modular design strategy implies the resolution of various tradeoffs. Depending on how such tradeoffs are solved, different organisational arrangements may be created to cope with ‘residual’ interdependencies. Hence, there is no need to postulate a perfect isomorphism, as some recent literature has proposed, between modularity at the product level and modularity at the organisational level

An Empirical Study of Cohesion and Coupling: Balancing Optimisation and Disruption

Search based software engineering has been extensively applied to the problem of finding improved modular structures that maximise cohesion and minimise coupling. However, there has, hitherto, been no longitudinal study of developers’ implementations, over a series of sequential releases. Moreover, results validating whether developers respect the fitness functions are scarce, and the potentially disruptive effect of search-based remodularisation is usually overlooked. We present an empirical study of 233 sequential releases of 10 different systems; the largest empirical study reported in the literature so far, and the first longitudinal study. Our results provide evidence that developers do, indeed, respect the fitness functions used to optimise cohesion/coupling (they are statistically significantly better than arbitrary choices with p << 0.01), yet they also leave considerable room for further improvement (cohesion/coupling can be improved by 25% on average). However, we also report that optimising the structure is highly disruptive (on average more than 57% of the structure must change), while our results reveal that developers tend to avoid such disruption. Therefore, we introduce and evaluate a multi-objective evolutionary approach that minimises disruption while maximising cohesion/coupling improvement. This allows developers to balance reticence to disrupt existing modular structure, against their competing need to improve cohesion and coupling. The multi-objective approach is able to find modular structures that improve the cohesion of developers’ implementations by 22.52%, while causing an acceptably low level of disruption (within that already tolerated by developers)

ArchOptions: A Real Options-Based Model for Predicting the Stability of Software Architectures

Architectural stability refers to the extent an architecture is flexible to endure evolutionary changes in stakeholders\' requirements and the environment. We assume that the primary goal of software architecture is to guide the system\'s evolution. We contribute to a novel model that exploits options theory to predict architectural stability. The model is predictive: it provides \"insights\" on the evolution of the software system based on valuing the extent an architecture can endure a set of likely evolutionary changes. The model builds on Black and Scholes financial options theory (Noble Prize wining) to value such extent. We show how we have derived the model: the analogy and assumptions made to reach the model, its formulation, and possible interpretations. We refer to this model as ArchOptions

Legacy Software Restructuring: Analyzing a Concrete Case

Software re-modularization is an old preoccupation of reverse engineering research. The advantages of a well structured or modularized system are well known. Yet after so much time and efforts, the field seems unable to come up with solutions that make a clear difference in practice. Recently, some researchers started to question whether some basic assumptions of the field were not overrated. The main one consists in evaluating the high-cohesion/low-coupling dogma with metrics of unknown relevance. In this paper, we study a real structuring case (on the Eclipse platform) to try to better understand if (some) existing metrics would have helped the software engineers in the task. Results show that the cohesion and coupling metrics used in the experiment did not behave as expected and would probably not have helped the maintainers reach there goal. We also measured another possible restructuring which is to decrease the number of cyclic dependencies between modules. Again, the results did not meet expectations

Using Modularity Metrics to assist Move Method Refactoring of Large System

For large software systems, refactoring activities can be a challenging task, since for keeping component complexity under control the overall architecture as well as many details of each component have to be considered. Product metrics are therefore often used to quantify several parameters related to the modularity of a software system. This paper devises an approach for automatically suggesting refactoring opportunities on large software systems. We show that by assessing metrics for all components, move methods refactoring an be suggested in such a way to improve modularity of several components at once, without hindering any other. However, computing metrics for large software systems, comprising thousands of classes or more, can be a time consuming task when performed on a single CPU. For this, we propose a solution that computes metrics by resorting to GPU, hence greatly shortening computation time. Thanks to our approach precise knowledge on several properties of the system can be continuously gathered while the system evolves, hence assisting developers to quickly assess several solutions for reducing modularity issues