A Novel Approach To Predict The Cross Phase Based Ensemble Decision Making And Privacy Preserved For Defect Detection Using Sdlc Software Metrics
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A Novel Approach To Predict The Cross Phase Based Ensemble Decision Making And Privacy Preserved For Defect Detection Using Sdlc Software Metrics
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Author : Dr. NAGESWARA RAO MOPARTHI
language : en
Publisher: OrangeBooks Publication
Release Date : 2024-10-25
A Novel Approach To Predict The Cross Phase Based Ensemble Decision Making And Privacy Preserved For Defect Detection Using Sdlc Software Metrics written by Dr. NAGESWARA RAO MOPARTHI and has been published by OrangeBooks Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-25 with Computers categories.
Software defects are always found to be a major cause of failure As the researchers continue to proceed with the use of data mining technology, Here we have used data in different software life cycle phases for defect prediction. In this proposed approach, we have performed robust preprocessing and defects detection algorithm on the metrics data. This approach effectively handles the uncertain data and transforms the data for defect detection. Finally, the proposed defect detection model was applied to the transformed data to detect the metric decision patterns.
Automated Programming Frameworks For Analyzing Differential Privacy
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Author : Yuxin Wang
language : en
Publisher:
Release Date : 2022
Automated Programming Frameworks For Analyzing Differential Privacy written by Yuxin Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
The accelerating growth of data has led to fruitful researches and real-world applications. While large datasets have benefited performance in many fields, recent incidents of data leakages and abuses have raised public concerns for data privacy. It has become a vital yet challenging task to balance individuals' privacy and data utilization for both researchers and data analysts. Among many attempts to tackle this challenge, differential privacy has become a de facto standard that provides a promising way to release individuals' sensitive data in a privacy-preserving manner. However, designing differentially-private algorithms is notoriously difficult and error-prone. Significant errors have happened even in peer-reviewed papers and systems. Such mistakes have led to researches on automated analysis of differential privacy algorithms to aid developers in the system design process. However, the limitations of existing tools either make the analysis time-consuming or fail to analyze sophisticated systems designed for differential privacy. In this dissertation, we propose a set of novel programming frameworks that target at three major aspects of automated analysis of differential privacy: verification, counterexample detection and program synthesis. For verification, we develop ShadowDP that embeds a novel proving technique named Shadow Execution to enable verification of a complex algorithm Report Noisy Max with very few annotations. Unlike prior works, ShadowDP is built upon standard program logics, making it easy to offload the verification of differential privacy to off-the-shelf verifiers. Our evaluations show ShadowDP is more efficient by orders of magnitude, compared with existing verifiers for differential privacy. For counterexample detection when a system fails to satisfy differential privacy, we propose CheckDP, the first integrated framework to prove and disprove differential privacy. A novel bidirectional Counterexample-Guided Inductive Synthesis (CEGIS) is developed and embedded in CheckDP, enabling it to simultaneously generate a proof for correct systems, as well as a counterexample for incorrect systems. Lastly, we develop DPGen, an automated synthesizer with customizable utility metrics for differential privacy. DPGen employs a novel approach to generate sketch programs and models the synthesis problem as an optimization problem involving privacy and utility, making it flexible and efficient in generating differentially-private programs with different requirements.
Intelligent Software Defect Prediction
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Author : Xiao-Yuan Jing
language : en
Publisher: Springer Nature
Release Date : 2024-01-17
Intelligent Software Defect Prediction written by Xiao-Yuan Jing and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-17 with Computers categories.
With the increasing complexity of and dependency on software, software products may suffer from low quality, high prices, be hard to maintain, etc. Software defects usually produce incorrect or unexpected results and behaviors. Accordingly, software defect prediction (SDP) is one of the most active research fields in software engineering and plays an important role in software quality assurance. Based on the results of SDP analyses, developers can subsequently conduct defect localization and repair on the basis of reasonable resource allocation, which helps to reduce their maintenance costs. This book offers a comprehensive picture of the current state of SDP research. More specifically, it introduces a range of machine-learning-based SDP approaches proposed for different scenarios (i.e., WPDP, CPDP, and HDP). In addition, the book shares in-depth insights into current SDP approaches’ performance and lessons learned for future SDP research efforts. We believe thesetheoretical analyses and emerging challenges will be of considerable interest to all researchers, graduate students, and practitioners who want to gain deeper insights into and/or find new research directions in SDP. It offers a comprehensive introduction to the current state of SDP and detailed descriptions of representative SDP approaches.