48 research outputs found

    A Source-Code Maintainability Evaluation Model for Software Products

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    The maintainability index (MI) has been proposed to calculate a single number which expresses the maintainability of a system. This article presents a model for evaluating the maintainability of software products. The model improves the shortcomings observed in the maintainability assessment approaches in the quality assessment models SQuaRE (ISO25000), ISO 9126, Squale and the FCM standard. Its main innovation is to take into account the importance of entities in the system when calculating the maintainability score. This implies that the same type of defect will have a different score depending on the entity presenting it. Seven experts with several years of experience evaluated the model. They confirmed the effectiveness and usability of the model. Then, we compared our model with the Squale maintainability index and the classical maintainability index. The results show no correlation between these models. The implications are that each method gives a slightly different view of maintainability

    Extracteur aléatoires multi-sources sur les corps finis et les courbes elliptiques

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    International audienceWe propose two-sources randomness extractors over finite fields and on elliptic curves that can extract from two sources of information without consideration of other assumptions that the starting algorithmic assumptions with a competitive level of security. These functions have several applications. We propose here a description of a version of a Diffie-Hellman key exchange protocol and key extraction.Nous proposons des extracteurs d'aléas 2-sources sur les corps finis et sur les courbes elliptiques capables d'extraire à partir de plusieurs sources d'informations sans considération d'autres hypothèses que les hypothèses algorithmiques de départ avec un niveau de sécurité compétitif. Ces fonctions possèdent plusieurs applications. Nous proposons ici une version du protocole d'échange de clé Diffie-Hellman incluant la phase d'extraction

    A Dynamic Application Partitioning and Offloading Framework to Enhance the Capabilities of Transient Clouds Using Mobile Agents

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    Mobile cloud computing has emerged as a prominent area of research, a natural extension of cloud computing that proposes to offer solutions for enhancing the capabilities of smart mobile devices commonly plagued by resource constraints. As one of its promising models, transient clouds aim to address the internet connectivity shortfall inherent in most solutions through the formation of ad hoc networks by devices in close proximity, then the offloading some computations (Cyber Foraging) to the created cloud. However, transient clouds, at their current state, have several limitations, concerning their expansion on a local network having a large number of devices and the management of the instability of the network due to the constant mobility of the devices. Another issue is the fact code partitioning and offloading are not addressed to fit the need of such networks, thereby rendering the distributed computing mechanism barely efficient for the Transient Cloud. In this study, we propose a transient cloud-based framework that exploits the use of multi-agent systems, enabling a dynamic partitioning and offloading of code, and facilitating the movement and the execution of code partition packets in a multi-hop ad-hoc mesh network. When created and deployed, these intelligent mobile agents operate independently or collaboratively and adapt to the continual entry and exit of devices in the neighbourhood. The integration of these trending concepts in distributed computing within a framework offers a new architecture for resource-sharing among cooperating devices that addresses the varied issues that arise in dynamic environments

    Detection of breast pathologies in digital mammography images by thresholding and mathematical morphology

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    This paper proposes an algorithm for mass and micro-calcification detection by manual thresholding and prewitt detector. This algorithm has been tested using mammography images of different densities from multiple databases of a health clinic and images taken from the internet (40 images in total). The results are very accurate, allowing better detection of breast pathologies (mass and micro-calcification). Finally, the detection of breast pathologies was performed using as input a detection algorithm specially designed for this purpose. After segmentation by manual thresholding, morphological opening, morphological dilatation and Prewitt contour detection we have a demarcation of the masses and breast micro-calcification. The results obtained show the robustness of the proposed manual thresholding method. In order to evaluate the efficiency of our pathology detector, we compared our results with those in the literature and performed a qualitative evaluation with a rate of 98.04% for the detection of breast pathologies.  A radiologist from the health clinic evaluated the results and considers them acceptable to the CAD

    Identifying the exact fixing actions of static rule violation

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    International audience—We study good programming practices expressed in rules and detected by static analysis checkers such as PMD or FindBugs. To understand how violations to these rules are corrected and whether this can be automated, we need to identify in the source code where they appear and how they were fixed. This presents some similarities with research on understanding software bugs, their causes, their fixes, and how they could be avoided. The traditional method to identify how a bug or a rule violation were fixed consists in finding the commit that contains this fix and identifying what was changed in this commit. If the commit is small, all the lines changed are ascribed to the fixing of the rule violation or the bug. However, commits are not always atomic, and several fixes and even enhancements can be mixed in a single one (a large commit). In this case, it is impossible to detect which modifications contribute to which fix. In this paper, we are proposing a method that identifies precisely the modifications that are related to the correction of a rule violation. The same method could be applied to bug fixes, providing there is a test illustrating this bug. We validate our solution on a real world system and actual rules

    Automatic application watershed in early detection and classification masses in mammography image using machine learning methods

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    Mammogram images are used by radiologists for the diagnosis of breast cancer. However, the interpretation of these images remains difficult depending on the type of breast, especially those of dense breasts, which are difficult to read, as they may contain abnormal structures similar to normal breast tissue and could lead to a high rate of false positives and false negatives. In this paper, we present an efficient computer-aided diagnostic system for the detection and classification of breast masses. After removing noise and artefacts from the images using 2D median filtering, mathematical morphology and pectoral muscle removal by Hough's algorithm, the resulting image is used for breast mass segmentation using the watershed algorithm. Thus, after the segmentation, the help system extracts several data by the wavelet transform and the co-occurrence matrix (GLCM) to finally lead to a classification in terms of malignant and benign mass via the Support Vector Machine (SVM) classifier. This method was applied on 48 MLO images from the image base (mini-MIAS) and the results obtained from this proposed system is 93,75% in terms of classification rate, 88% in terms of sensitivity and a specificity of 94%