Modularity is a desirable characteristic for software systems. In this article we propose to use a quantitative method from complex network sciences to estimate the coherence between the modularity of the dependency network of large open source Java projects and their decomposition in terms of Java packages. The results presented in this article indicate that our methodology offers a promising and reasonable quantitative approach with potential impact on software engineering processes.
Another possible, complementary, reason for fluctuations are refactoring events, where software is usually rewritten or restructured in order to improve multiple features such as functionality, flexibility, reusability or structural quality. Such events could lead to the sudden jumps observed in Figure along the time evolution of a software project. In @cite_13 , refactoring metrics are proposed which take into account the dynamics of changing code. This line of research is well aligned with our purposes and can be easily adapted and augmented by our network perspective on software development processes.
@cite_13
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Software developers often duplicate source code to replicate functionality. This practice can hinder the maintenance of a software project: bugs may arise when two identical code segments are edited inconsistently. This paper presents DejaVu, a highly scalable system for detecting these general syntactic inconsistency bugs. DejaVu operates in two phases. Given a target code base, a parallel inconsistent clone analysis first enumerates all groups of source code fragments that are similar but not identical. Next, an extensible buggy change analysis framework refines these results, separating each group of inconsistent fragments into a fine-grained set of inconsistent changes and classifying each as benign or buggy. On a 75+ million line pre-production commercial code base, DejaVu executed in under five hours and produced a report of over 8,000 potential bugs. Our analysis of a sizable random sample suggests with high likelihood that at this report contains at least 2,000 true bugs and 1,000 code smells. These bugs draw from a diverse class of software defects and are often simple to correct: syntactic inconsistencies both indicate problems and suggest solutions. Software decay is a phenomenon that plagues aging software systems. While in recent years, there has been significant progress in the area of automatic detection of "code smells" on one hand, and code refactorings on the other hand, we claim that existing restructuring practices are seriously hampered by their symptomatic and informal (non-repeatable) nature. This paper makes a clear distinction between structural problems and structural symptoms (also known as code smells), and presents a novel, causal approach to restructuring object oriented systems. Our approach is based on two innovations: the encapsulation of correlations of symptoms and additional contextual information into higher-level design problems, and the univocal, explicit mapping of problems to unique refactoring solutions. Due to its explicit, repeatable nature, the approach shows high potential for increased levels of automation in the restructuring process, and consequently a decrease in maintenance costs. To study the impact of code clones on software quality, researchers typically carry out their studies based on fine-grained analysis of inconsistent changes at the revision level. As a result, they capture much of the chaotic and experimental nature inherent in any on-going software development process. Analyzing highly fluctuating and short-lived clones is likely to exaggerate the ill effects of inconsistent changes on the quality of the released software product, as perceived by the end user. To gain a broader perspective, we perform an empirical study on the effect of inconsistent changes on software quality at the release level. Based on a case study on three open source software systems, we observe that only 1.02 -4.00 of all clone genealogies introduce software defects at the release level, as opposed to the substantially higher percentages reported by previous studies at the revision level. Our findings suggest that clones do not have a significant impact on the post-release quality of the studied systems, and that the developers are able to effectively manage the evolution of cloned code. Recently, the phenomenon of software aging, one in which the state of the software system degrades with time, has been reported. This phenomenon, which may eventually lead to system performance degradation and or crash hang failure, is the result of exhaustion of operating system resources, data corruption, and numerical error accumulation. To counteract software aging, a technique called software rejuvenation has been proposed, which essentially involves occasionally terminating an application or a system, cleaning its internal state and or its environment, and restarting it. Since rejuvenation incurs an overhead, an important research issue is to determine optimal times to initiate this action. In this paper, we first describe how to include faults attributed to software aging in the framework of Gray's software fault classification (deterministic and transient), and study the treatment and recovery strategies for each of the fault classes. We then construct a semi-Markov reward model based on workload and resource usage data collected from the UNIX operating system. We identify different workload states using statistical cluster analysis, estimate transition probabilities, and sojourn time distributions from the data. Corresponding to each resource, a reward function is then defined for the model based on the rate of resource depletion in each state. The model is then solved to obtain estimated times to exhaustion for each resource. The result from the semi-Markov reward model are then fed into a higher-level availability model that accounts for failure followed by reactive recovery, as well as proactive recovery. This comprehensive model is then used to derive optimal rejuvenation schedules that maximize availability or minimize downtime cost.