Patterns of Component Evolution

Abstract

Contemporary software systems are composed of many components, which, in general, undergo phased and incremental development. In order to facilitate the corresponding construction process, it is important that the development team in charge has a good understanding of how individual software components typically evolve. Furthermore, software engineers need to be able to recognize abnormal patterns of growth with respect to size, structure, and complexity of the components and the resulting composite. Only if a development team understands the processes that underpin the evolution of software systems, will they be able to make better development choices. In this paper, we analyze recurring structural and evolutionary patterns that we have observed in public-domain software systems built using object-oriented programming languages. Based on our analysis, we discuss common growth patterns found in present-day component-based software systems and illustrate simple means to aid developers in achieving a better understanding of those patterns. As a consequence, we hope to raise the awareness level in the community on how component-based software systems tend to naturally evolve.

Comparative analysis of evolving software systems using the Gini coefficient

“…Typically [29,30] software systems follow a simple pattern: a few software entities contain much of the complexity and functionality, whereas the others define simple data abstractions and utilities. Most metrics are skewed, but skewed in different ways, making comparisons based on average and standard deviation largely meaningless.…”

“…In-Degree Count and Out-Degree Count reveal the coupling of classes within a system. These measures are extracted from the type dependency graph [29] that we construct for each analyzed system. The vertices in this graph are classes, whereas the edges are directed links between classes.…”

“…However, we are only beginning to understand * In Proceedings of the 25th International Conference on Software Maintenance (ICSM 2009), pp. 179-188, IEEE Computer Society, Los Alamitos, CA, USA, 2009. how change and distribution of functionality affect evolving software systems [23,29,30].…”

Partition Refinement of Component Interaction Automata: Why Structure Matters More Than Size

“…We can measure these structural features using two concepts: the scaling exponent β [27,26] to denote the power-scaling relationship between the number of states and the number of transitions of an automaton and the Gini coefficients [10,25] of the incoming and outgoing transitions in an automaton in order to quantify the degree of inequality in the distribution of these attributes in a given automaton. Both measures provide suitable summary metrics of the underlying directed graph topology of an automaton.…”

“…It order to identify the reasons as to why partition refinement can sometimes yield strong state space reduction ratios [16], while it fails completely on other occasions, we have run an analysis on a sample of 1,680 experimental composite systems. Each system consists of between 2 and 11 machine-generated Component Interaction Automata specifications that all enjoy topological properties similar to real-world software systems [27,26]. Every experiment was allowed to run at most two hours and was carried out on a Mac Pro equipped with one 2.66 GHz Quad-Core processor and 8GB 1066 MHz DDR3 memory running Mac OS X 10.6.3.…”