Global ETD Search

1	Assessing Software Defects using Nano-Patterns Detection Deo, Ajay Kumar 09 May 2015 (has links) Defects in software systems directly impact a product’s quality and overall customer satisfaction. Assessing defective code for the purpose of locating vulnerable areas and improving software quality and reliability is important for sustained software development efforts. Over the years, various techniques have been used to determine the likelihood that code fragments contain defects, such as identifying code smells, but these techniques have drawbacks. There is a need for better approaches. This thesis assesses software defects using nano-patterns by demonstrating that certain categories of nano-patterns are more defect-prone than others. We studied three open source systems from the Apache Software Foundation and found that ObjectCreator, FieldReader, TypeManipulator, Looping, Exceptions, LocalReader, and LocalWriter nano-patters are more defect-prone than others. Apart from assessing software defects, we expect this new finding will contribute to further research on other applications of nano-patterns and improve coding practices. Software Quality Software Patterns Traceable Patterns Nano-Patterns
2	A Software Vulnerability Prediction Model Using Traceable Code Patterns And Software Metrics Sultana, Kazi Zakia 10 August 2018 (has links) Software security is an important aspect of ensuring software quality. The goal of this study is to help developers evaluate software security at the early stage of development using traceable patterns and software metrics. The concept of traceable patterns is similar to design patterns, but they can be automatically recognized and extracted from source code. If these patterns can better predict vulnerable code compared to the traditional software metrics, they can be used in developing a vulnerability prediction model to classify code as vulnerable or not. By analyzing and comparing the performance of traceable patterns with metrics, we propose a vulnerability prediction model. Objective: This study explores the performance of code patterns in vulnerability prediction and compares them with traditional software metrics. We have used the findings to build an effective vulnerability prediction model. Method: We designed and conducted experiments on the security vulnerabilities reported for Apache Tomcat (Releases 6, 7 and 8), Apache CXF and three stand-alone Java web applications of Stanford Securibench. We used machine learning and statistical techniques for predicting vulnerabilities of the systems using traceable patterns and metrics as features. Result: We found that patterns have a lower false negative rate and higher recall in detecting vulnerable code than the traditional software metrics. We also found a set of patterns and metrics that shows higher recall in vulnerability prediction. Conclusion: Based on the results of the experiments, we proposed a prediction model using patterns and metrics to better predict vulnerable code with higher recall rate. We evaluated the model for the systems under study. We also evaluated their performance in the cross-dataset validation. vulnerability software security software quality software testing software metrics nano-patterns micro patterns
3	Modelling and simulation of surface morphology driven by ion bombardment / Modellieren und Simulation der Oberflächenmorphologie gefahren durch Ionenbombardierung Yewande, Emmanuel Oluwole 02 May 2006 (has links) No description available. 530 Physik Mathematics and Computer Science Non-equilibrium Oberflächen Ionenbombardierung Selbst--organisierte nano Muster Monte-Carlo Simulation Kontinuumtheorie die coarsening Kräuselung Topographie Quantenpunkt Non-equilibrium surfaces ion bombardment self-organized nano patterns Monte Carlo simulation continuum theory ripple coarsening topography quantum dot 33.06 33.10 33.68 RTF 400: Ionenimplantation {Physik}

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