A FRAMEWORK FOR SOFTWARE RELIABILITY MANAGEMENT BASED ON THE SOFTWARE DEVELOPMENT PROFILE MODEL

Recent empirical studies of software have shown a strong correlation between change history of files and their fault-proneness. Statistical data analysis techniques, such as regression analysis, have been applied to validate this finding. While these regression-based models show a correlation between selected software attributes and defect-proneness, in most cases, they are inadequate in terms of demonstrating causality. For this reason, we introduce the Software Development Profile Model (SDPM) as a causal model for identifying defect-prone software artifacts based on their change history and software development activities. The SDPM is based on the assumption that human error during software development is the sole cause for defects leading to software failures. The SDPM assumes that when a software construct is touched, it has a chance to become defective. Software development activities such as inspection, testing, and rework further affect the remaining number of software defects. Under this assumption, the SDPM estimates the defect content of software artifacts based on software change history and software development activities. SDPM is an improvement over existing defect estimation models because it not only uses evidence from current project to estimate defect content, it also allows software managers to manage software projects quantitatively by making risk informed decisions early in software development life cycle. We apply the SDPM in several real life software development projects, showing how it is used and analyzing its accuracy in predicting defect-prone files and compare the results with the Poisson regression model

A HOLISTIC REDUNDANCY- AND INCENTIVE-BASED FRAMEWORK TO IMPROVE CONTENT AVAILABILITY IN PEER-TO-PEER NETWORKS

Peer-to-Peer (P2P) technology has emerged as an important alternative to the traditional client-server communication paradigm to build large-scale distributed systems. P2P enables the creation, dissemination and access to information at low cost and without the need of dedicated coordinating entities. However, existing P2P systems fail to provide high-levels of content availability, which limit their applicability and adoption. This dissertation takes a holistic approach to device mechanisms to improve content availability in large-scale P2P systems. Content availability in P2P can be impacted by hardware failures and churn. Hardware failures, in the form of disk or node failures, render information inaccessible. Churn, an inherent property of P2P, is the collective effect of the users’ uncoordinated behavior, which occurs when a large percentage of nodes join and leave frequently. Such a behavior reduces content availability significantly. Mitigating the combined effect of hardware failures and churn on content availability in P2P requires new and innovative solutions that go beyond those applied in existing distributed systems. To addresses this challenge, the thesis proposes two complementary, low cost mechanisms, whereby nodes self-organize to overcome failures and improve content availability. The first mechanism is a low complexity and highly flexible hybrid redundancy scheme, referred to as Proactive Repair (PR). The second mechanism is an incentive-based scheme that promotes cooperation and enforces fair exchange of resources among peers. These mechanisms provide the basis for the development of distributed self-organizing algorithms to automate PR and, through incentives, maximize their effectiveness in realistic P2P environments. Our proposed solution is evaluated using a combination of analytical and experimental methods. The analytical models are developed to determine the availability and repair cost properties of PR. The results indicate that PR’s repair cost outperforms other redundancy schemes. The experimental analysis was carried out using simulation and the development of a testbed. The simulation results confirm that PR improves content availability in P2P. The proposed mechanisms are implemented and tested using a DHT-based P2P application environment. The experimental results indicate that the incentive-based mechanism can promote fair exchange of resources and limits the impact of uncooperative behaviors such as “free-riding”

Mining and untangling change genealogies

Developers change source code to add new functionality, fix bugs, or refactor their code. Many of these changes have immediate impact on quality or stability. However, some impact of changes may become evident only in the long term. This thesis makes use of change genealogy dependency graphs modeling dependencies between code changes capturing how earlier changes enable and cause later ones. Using change genealogies, it is possible to: (a) applyformalmethodslikemodelcheckingonversionarchivestorevealtemporal process patterns. Such patterns encode key features of the software process and can be validated automatically: In an evaluation of four open source histories, our prototype would recommend pending activities with a precision of 60—72%. (b) classify the purpose of code changes. Analyzing the change dependencies on change genealogies shows that change genealogy network metrics can be used to automatically separate bug fixing from feature implementing code changes. (c) build competitive defect prediction models. Defect prediction models based on change genealogy network metrics show competitive prediction accuracy when compared to state-of-the-art defect prediction models. As many other approaches mining version archives, change genealogies and their applications rely on two basic assumptions: code changes are considered to be atomic and bug reports are considered to refer to corrective maintenance tasks. In a manual examination of more than 7,000 issue reports and code changes from bug databases and version control systems of open- source projects, we found 34% of all issue reports to be misclassified and that up to 15% of all applied issue fixes consist of multiple combined code changes serving multiple developer maintenance tasks. This introduces bias in bug prediction models confusing bugs and features. To partially solve these issues we present an approach to untangle such combined changes with a mean success rate of 58—90% after the fact.Softwareentwickler ändern Source-Code um neue Funktionalität hinzuzufügen, Bugs zu beheben oder um ihren Code zu restrukturieren. Viele dieser Änderungen haben einen direkten Einfluss auf Qualität und Stabilität des Softwareprodukts. Jedoch kommen einige dieser Einflüsse erst zu einem späteren Zeitpunkt zur Geltung. Diese Arbeit verwendet Genealogien zwischen Code-Änderungen um zu erfassen, wie frühere Änderungen spätere Änderungen erfordern oder ermöglichen. Die Verwendung von Änderungs-Genealogien ermöglicht: (a) die Anwendung formaler Methoden wie Model-Checking auf Versionsarchive um temporäre Prozessmuster zu erkennen. Solche Prozessmuster verdeutlichen Hauptmerkmale eines Softwareentwicklungsprozesses: In einer Evaluation auf vier Open-Source Projekten war unser Prototyp im Stande noch ausstehende Änderungen mit einer Präzision von 60–72% vorherzusagen. (b) die Absicht einer Code-Änderung zu bestimmen. Analysen von Änderungsabhängigkeiten zeigen, dass Netzwerkmetriken auf Änderungsgenealogien geeignet sind um fehlerbehebende Änderungen von Änderungen die eine Funktionalität hinzufügen zu trennen. (c) konkurrenzfähige Fehlervorhersagen zu erstellen. Fehlervorhersagen basierend auf Genealogie-Metriken können sich mit anerkannten Fehlervorhersagemodellen messen. Änderungs-Genealogien und deren Anwendungen basieren, wie andere Data-Mining Ansätze auch, auf zwei fundamentalen Annahmen: Code-Änderungen beabsichtigen die Lösung nur eines Problems und Bug-Reports weisen auf Fehler korrigierende Tätigkeiten hin. Eine manuelle Inspektion von mehr als 7.000 Issue-Reports und Code-Änderungen hat ergeben, dass 34% aller Issue-Reports falsch klassifiziert sind und dass bis zu 15% aller fehlerbehebender Änderungen mehr als nur einem Entwicklungs-Task dienen. Dies wirkt sich negativ auf Vorhersagemodelle aus, die nicht mehr klar zwischen Bug-Fixes und anderen Änderungen unterscheiden können. Als Lösungsansatz stellen wir einen Algorithmus vor, der solche nicht eindeutigen Änderungen mit einer Erfolgsrate von 58–90% entwirrt