Software developers must produce software that can be changed without the risk of degrading the software architecture. One way to address software changes is to classify their causes and effects. A software change classification mechanism allows engineers to develop a common approach for handling changes. This information can be used to show the potential impact of the change. The goal of this research is to develop a change classification scheme that can be used to address causes of architectural degradation. This scheme can be used to model the effects of changes to software architecture. This research also presents a study of the initial architecture change classification scheme. The results of the study indicated that the classification scheme was easy to use and provided some benefit to developers. In addition, the results provided some evidence that changes of different types (in this classification scheme) required different amounts of effort to implement.

Academia and industry expend much effort to predict software defects. Researchers proposed many defect prediction algorithms and metrics. While previous defect prediction techniques often take the author of the code into consideration, none of these techniques build a separate prediction model for each developer. Different developers have different coding styles, commit frequencies, and experience levels, which would result in different defect patterns. When the defects of different developers are combined, such differences are obscured, hurting the prediction performance. This thesis proposes two techniques to improve defect prediction performance: personalized defect prediction and confidence-based hybrid defect prediction. Personalized defect prediction builds a separate prediction model for each developer to predict software defects. Confidence-based hybrid defect prediction combines different models by picking the prediction from the model with the highest confidence. As a proof of concept, we apply the two techniques to classify defects at the file change level. We implement the state-of-the-art change classification as the baseline and compare with the personalized defect prediction approach. Confidence-based defect prediction combines these two models. We evaluate on six large and popular software projects written in C and Java—the Linux kernel, PostgreSQL, Xorg, Eclipse, Lucene and Jackrabbit.

Academia and industry expend much effort to predict software defects. Researchers proposed many defect prediction algorithms and metrics. While previous defect prediction techniques often take the author of the code into consideration, none of these techniques build a separate prediction model for each developer. Different developers have different coding styles, commit frequencies, and experience levels, which would result in different defect patterns. When the defects of different developers are combined, such differences are obscured, hurting the prediction performance. This thesis proposes two techniques to improve defect prediction performance: personalized defect prediction and confidence-based hybrid defect prediction. Personalized defect prediction builds a separate prediction model for each developer to predict software defects. Confidence-based hybrid defect prediction combines different models by picking the prediction from the model with the highest confidence. As a proof of concept, we apply the two techniques to classify defects at the file change level. We implement the state-of-the-art change classification as the baseline and compare with the personalized defect prediction approach. Confidence-based defect prediction combines these two models. We evaluate on six large and popular software projects written in C and Java—the Linux kernel, PostgreSQL, Xorg, Eclipse, Lucene and Jackrabbit.

Service Oriented Virtual Organizations (SOVOs) business processes and services are subject to change to meet the internal and external requirements of the competitive, complex and rapidly changing environment they operate in. More practical and efficient ways of change management are needed to allow different partners to initiate changes to their business process and services in a faster and user-transparent manner. This thesis proposes a Change Management Framework for service oriented virtual organizations including a structural and a procedural framework. The structural framework categorizes changes in the SOVO into three layers of change; which include the value network layer, the collaborative process layer and the service providers’ layer, and identifies the impact of change on each layer. Furthermore, the structural framework identifies various triggers of changes which eventually lead to actions taken at the three layers. The change management procedural framework is derived from the ITIL V3, ECM and ECOLEAD best practices and recommendations, customized to fit the SOVO change requirements. It provides different components including the six layers for change processes, change control, change actors and related management processes. The change management procedural framework provides a sequence of steps and methods that the SOVO and its participated organizations can follow in initiating changes to their business processes or services. We design an implementation architecture and a prototype for building the change management console which enables the SOVO change management participants to initiate, assess, collaborate, monitor and authorize changes. The prototype is developed to realize and validate the change management process of change in the SOVO environment. We employ the various capabilities of the IBM Business Process Management (BPM) (including its recent Web 2.0 capabilities) to increase the collaboration between partners in the process of change. We demonstrate that the proposed solutions facilitate and enhance the process of change by effectively engaging the SOVO partners in the process of change.

L’imagerie hyperspectrale est un type d’imagerie émergent qui connaît un essor important depuis le début des années 2000. Grâce à une structure spectrale très fine qui produit un volume de donnée très important, elle apporte, par rapport à l’imagerie visible classique, un supplément d’information pouvant être mis à profit dans de nombreux domaines d’exploitation. Nous nous intéressons spécifiquement à la détection et l’analyse de changements entre deux images de la même scène, pour des applications orientées vers la défense.Au sein de ce manuscrit, nous commençons par présenter l’imagerie hyperspectrale et les contraintes associées à son utilisation pour des problématiques de défense. Nous présentons ensuite une méthode de détection et de classification de changements basée sur la recherche de directions spécifiques dans l’espace généré par le couple d’images, puis sur la fusion des directions proches. Nous cherchons ensuite à exploiter l’information obtenue sur les changements en nous intéressant aux possibilités de dé-mélange de séries temporelles d’images d’une même scène. Enfin, nous présentons un certain nombre d’extensions qui pourront être réalisées afin de généraliser ou améliorer les travaux présentés et nous concluons. / Hyperspectral imagery is an emerging imagery technology which has known a growing interest since the 2000’s. This technology allows an impressive growth of the data registered from a specific scene compared to classical RGB imagery. Indeed, although the spatial resolution is significantly lower, the spectral resolution is very small and the covered spectral area is very wide. We focus on change detection between two images of a given scene for defense oriented purposes.In the following, we start by introducing hyperspectral imagery and the specificity of its exploitation for defence purposes. We then present a change detection and analysis method based on the search for specifical directions in the space generated by the image couple, followed by a merging of the nearby directions. We then exploit this information focusing on theunmixing capabilities of multitemporal hyperspectral data. Finally, we will present a range of further works that could be done in relation with our work and conclude about it.

Service Oriented Virtual Organizations (SOVOs) business processes and services are subject to change to meet the internal and external requirements of the competitive, complex and rapidly changing environment they operate in. More practical and efficient ways of change management are needed to allow different partners to initiate changes to their business process and services in a faster and user-transparent manner. This thesis proposes a Change Management Framework for service oriented virtual organizations including a structural and a procedural framework. The structural framework categorizes changes in the SOVO into three layers of change; which include the value network layer, the collaborative process layer and the service providers’ layer, and identifies the impact of change on each layer. Furthermore, the structural framework identifies various triggers of changes which eventually lead to actions taken at the three layers. The change management procedural framework is derived from the ITIL V3, ECM and ECOLEAD best practices and recommendations, customized to fit the SOVO change requirements. It provides different components including the six layers for change processes, change control, change actors and related management processes. The change management procedural framework provides a sequence of steps and methods that the SOVO and its participated organizations can follow in initiating changes to their business processes or services. We design an implementation architecture and a prototype for building the change management console which enables the SOVO change management participants to initiate, assess, collaborate, monitor and authorize changes. The prototype is developed to realize and validate the change management process of change in the SOVO environment. We employ the various capabilities of the IBM Business Process Management (BPM) (including its recent Web 2.0 capabilities) to increase the collaboration between partners in the process of change. We demonstrate that the proposed solutions facilitate and enhance the process of change by effectively engaging the SOVO partners in the process of change.