Network analysis has been successfully applied in software engineering to understand structural effects in the software. System software is represented as a network graph, and network metrics are used to analyse system quality. This study is motivated by a previous study, which represents the software structure as three‐node subgraphs and empirically identifies that software structure continuously evolves over system releases. Here, the authors extend the previous study to analyse the relation of structural evolution and the defectiveness of subgraphs in the software network graph. This study investigates the behaviour of subgraph defects through software evolution and their impact on system defectiveness. Statistical methods were used to study subgraph defectiveness across versions of the systems and across subgraph types. The authors conclude that software versions have similar behaviours in terms of average subgraph type defectiveness and subgraph frequency distributions. However, different subgraph types have different defectiveness distributions. Based on these conclusions, the authors motivate the use of subgraph‐based software representation in defect predictions and software modelling. These promising findings contribute to the further development of the software engineering discipline and help software developers and quality management in terms of better modelling and focusing their testing efforts within the code structure represented by subgraphs.

Background. The effects of different practices on fault distributions in evolving complex software systems is not fully understood. Software reuse and unit verification are practices used to improve system reliability by minimising the number of late faults. Reused software benefits from already being verified while unit verification aims to find faults early.Aims. We want to study effects of software reuse and unit verification on future modifications, fault densities of software units, and fault distributions.Method. We applied statistical analysis to a sample of 520 units that were reused and modified within four sequential projects from one product line in the telecommunication domain.Results. In reused units, the results of unit verification are correlated to a smaller degree of modifications and decreased fault densities.Conclusion. Unit verification in complex systems may improve system evolution in terms of smaller modifications and decrease of fault densities. The unit verification faults in reused components may be used as predictors of component modification and fault density.

eScience consists of computation-intensive workflows executing on highly distributed networks. Service compositions aggregate web services to automate scientific and enterprise business processes. Along with the increased demand for data quality and Quality of Service (QoS) for an accurate outcome in a shorter completion time, execution of the eScience workflows and service compositions are also required to be distributed efficiently across various geo-distributed nodes. This paper presents Mayan,1a Software-Defined Networking (SDN) based approach for service composition. Mayan i) facilitates an adaptive execution of scientific workflows, ii) offers a more efficient service composition by leveraging distributed execution frameworks, in addition to the traditional web service engines, and iii) enables a very large-scale reliable service composition by finding and consuming the current best-fit among the multiple implementations or deployments of the same service.

There exists a huge amount of vertical applications that are developed for isolated computing environments. Due to increasing demand for additional resources there is a clear need to adapt these applications to the distributed environments. However, this is not an easy task and numerous variants are possible. Moreover, in this transition a new quality requirements become important, such as application elasticity. Application elasticity has to be built into a software system to enable smooth cost optimization at the run-time. In this paper, we provide a framework for evaluating different transformation variants of vertical Java EE multi-tiered applications into elastic cloud applications. With support of this framework the software developer is guided how to transform its application achieving optimal elasticity strategy. The framework is evaluated on slicing and evaluating elasticity of existing SaaS multi-tiered Java application used in Croatian market.

We still do not have clear figure about how software systems evolve and how we may control its evolution process. Software structure has been identified that may have the biggest impact, especially because it may be represented from numerous perspectives. Novelty introduced in this paper is the way how we define the structure of evolving complex software systems. The structure is represented with help of graph representations, and subgraph frequencies, the concept reused from the network analysis theory. The graph structure of a software system and its evolution over the system versions, as well as its relation to defectiveness, is empirically studied in terms of subgraph frequencies and motifs for more than 30 releases of three large open source software systems. We identified that the same set of subgraphs of software system is present across the system version, but different sets, although overlapping, are present in different software systems. Furthermore, we confirmed the continuous system evolution in terms of continuous structure change and we find some evidence for its relation to system defectiveness.

Background: Software engineering is searching for general principles that apply across contexts, for example, to help guide software quality assurance. Fenton and Ohlsson presented such observations on fault distributions, which have been replicated once. Objectives: We aimed to replicate their study again to assess the robustness of the findings in a new environment, five years later. Method: We conducted a literal replication, collecting defect data from five consecutive releases of a large software system in the telecommunications domain, and conducted the same analysis as in the original study. Results: The replication confirms results on unevenly distributed faults over modules, and that fault proneness distributions persist over test phases. Size measures are not useful as predictors of fault proneness, while fault densities are of the same order of magnitude across releases and contexts. Conclusions: This replication confirms that the uneven distribution of defects motivates uneven distribution of quality assurance efforts, although predictors for such distribution of efforts are not sufficiently precise.