Assessing architectural evolution: A case study

This is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2011 SpringerThis paper proposes to use a historical perspective on generic laws, principles, and guidelines, like Lehman’s software evolution laws and Martin’s design principles, in order to achieve a multi-faceted process and structural assessment of a system’s architectural evolution. We present a simple structural model with associated historical metrics and visualizations that could form part of an architect’s dashboard. We perform such an assessment for the Eclipse SDK, as a case study of a large, complex, and long-lived system for which sustained effective architectural evolution is paramount. The twofold aim of checking generic principles on a well-know system is, on the one hand, to see whether there are certain lessons that could be learned for best practice of architectural evolution, and on the other hand to get more insights about the applicability of such principles. We find that while the Eclipse SDK does follow several of the laws and principles, there are some deviations, and we discuss areas of architectural improvement and limitations of the assessment approach

Agent-based Simulation of Open Source Software Evolution

We present an agent-based simulation model of open source software (OSS). To our knowledge, this is the first model of OSS evolution that includes four significant factors: productivity limited by the complexity of software modules, the software's fitness for purpose, the motivation of developers, and the role of users in defining requirements. The model was evaluated by comparing the simulated results against four measures of software evolution (system size, proportion of highly complex modules, level of complexity control work, and distribution of changes) for four large OSS systems. The simulated results resembled all the observed data, including alternating periods of growth and stagnation. The fidelity of the model suggests that the factors included here have significant effects on the evolution of OSS systems

Empirical studies of open source evolution

Copyright @ 2008 Springer-VerlagThis chapter presents a sample of empirical studies of Open Source Software (OSS) evolution. According to these studies, the classical results from the studies of proprietary software evoltion, such as Lehman’s laws of software evolution, might need to be revised, if not fully, at least in part, to account for the OSS observations. The book chapter also summarises what appears to be the empirical status of each of Lehman’s laws with respect to OSS and highlights the threads to validity that frequently emerge in these empirical studies. The chapter also discusses related topics for further research

Structural Complexity and Decay in FLOSS Systems: An Inter-Repository Study

Past software engineering literature has firmly established that software architectures and the associated code decay over time. Architectural decay is, potentially, a major issue in Free/Libre/Open Source Software (FLOSS) projects, since developers sporadically joining FLOSS projects do not always have a clear understanding of the underlying architecture, and may break the overall conceptual structure by several small changes to the code base. This paper investigates whether the structure of a FLOSS system and its decay can also be influenced by the repository in which it is retained: specifically, two FLOSS repositories are studied to understand whether the complexity of the software structure in the sampled projects is comparable, or one repository hosts more complex systems than the other. It is also studied whether the effort to counteract this complexity is dependent on the repository, and the governance it gives to the hosted projects. The results of the paper are two-fold: on one side, it is shown that the repository hosting larger and more active projects presents more complex structures. On the other side, these larger and more complex systems benefit from more anti-regressive work to reduce this complexity

Agent-based simulation of open source evolution

We present an agent-based simulation model developed to study how size, complexity and effort relate to each other in the development of open source software (OSS). In the model, many developer agents generate, extend, and re-factor code modules independently and in parallel. This accords with empirical observations of OSS development. To our knowledge, this is the first model of OSS evolution that includes the complexity of software modules as a limiting factor in productivity, the fitness of the software to its requirements, and the motivation of developers. Validation of the model was done by comparing the simulated results against four measures of software evolution (system size, proportion of highly complex modules, level of complexity control work, and distribution of changes) for four large OSS systems. The simulated results resembled the observed data, except for system size: three of the OSS systems showed alternating patterns of super-linear and sub-linear growth, while the simulations produced only super-linear growth. However, the fidelity of the model for the other measures suggests that developer motivation and the limiting effect of complexity on productivity have a significant effect on the development of OSS systems and should be considered in any model of OSS development

Fast filtering and animation of large dynamic networks

Detecting and visualizing what are the most relevant changes in an evolving network is an open challenge in several domains. We present a fast algorithm that filters subsets of the strongest nodes and edges representing an evolving weighted graph and visualize it by either creating a movie, or by streaming it to an interactive network visualization tool. The algorithm is an approximation of exponential sliding time-window that scales linearly with the number of interactions. We compare the algorithm against rectangular and exponential sliding time-window methods. Our network filtering algorithm: i) captures persistent trends in the structure of dynamic weighted networks, ii) smoothens transitions between the snapshots of dynamic network, and iii) uses limited memory and processor time. The algorithm is publicly available as open-source software.Comment: 6 figures, 2 table

Class movement and re-location: An empirical study of Java inheritance evolution

This is the post-print version of the final paper published in Journal of Systems and Software. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2009 Elsevier B.V.Inheritance is a fundamental feature of the Object-Oriented (OO) paradigm. It is used to promote extensibility and reuse in OO systems. Understanding how systems evolve, and specifically, trends in the movement and re-location of classes in OO hierarchies can help us understand and predict future maintenance effort. In this paper, we explore how and where new classes were added as well as where existing classes were deleted or moved across inheritance hierarchies from multiple versions of four Java systems. We observed first, that in one of the studied systems the same set of classes was continuously moved across the inheritance hierarchy. Second, in the same system, the most frequent changes were restricted to just one sub-part of the overall system. Third, that a maximum of three levels may be a threshold when using inheritance in a system; beyond this level very little activity was observed, supporting earlier theories that, beyond three levels, complexity becomes overwhelming. We also found evidence of ‘collapsing’ hierarchies to bring classes up to shallower levels. Finally, we found that larger classes and highly coupled classes were more frequently moved than smaller and less coupled classes. Statistical evidence supported the view that larger classes and highly coupled classes were less cohesive than smaller classes and lowly coupled classes and were thus more suitable candidates for being moved (within an hierarchy)

Sustaining Economic Exploitation of Complex Ecosystems in Computational Models of Coupled Human-Natural Networks

Understanding ecological complexity has stymied scientists for decades. Recent elucidation of the famously coined "devious strategies for stability in enduring natural systems" has opened up a new field of computational analyses of complex ecological networks where the nonlinear dynamics of many interacting species can be more realistically mod-eled and understood. Here, we describe the first extension of this field to include coupled human-natural systems. This extension elucidates new strategies for sustaining extraction of biomass (e.g., fish, forests, fiber) from ecosystems that account for ecological complexity and can pursue multiple goals such as maximizing economic profit, employment and carbon sequestration by ecosystems. Our more realistic modeling of ecosystems helps explain why simpler "maxi-mum sustainable yield" bioeconomic models underpinning much natural resource extraction policy leads to less profit, biomass, and biodiversity than predicted by those simple models. Current research directions of this integrated natu-ral and social science include applying artificial intelligence, cloud computing, and multiplayer online games

A Survey on Software Testing Techniques using Genetic Algorithm

The overall aim of the software industry is to ensure delivery of high quality software to the end user. To ensure high quality software, it is required to test software. Testing ensures that software meets user specifications and requirements. However, the field of software testing has a number of underlying issues like effective generation of test cases, prioritisation of test cases etc which need to be tackled. These issues demand on effort, time and cost of the testing. Different techniques and methodologies have been proposed for taking care of these issues. Use of evolutionary algorithms for automatic test generation has been an area of interest for many researchers. Genetic Algorithm (GA) is one such form of evolutionary algorithms. In this research paper, we present a survey of GA approach for addressing the various issues encountered during software testing.Comment: 13 Page