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9th international conference on Cloud Computing, Data Science and engineering (CONFLUENCE)

作者： Singh, Prabhpahul Malhotra, Ruchika Bansal, Siddartha JP Inst Informat Technol Dept Comp Sci Noida India Delhi Technol Univ Dept Comp Sci & Engn Discipline Software Engn Delhi 110042 India Arista Networks Bengaluru 560103 Karnataka India

ISBN: (纸本)9781538659335

the quality of the software can be improved by determining its faulty portions in the initial phases of the lifecycle of a software product. there are various machine learning algorithms proposed in literature studies that can be used to predict faulty classes. the machine learning algorithms determine faulty classes by using object oriented metrics as predictors. these models will allow the developers to predict faulty classes and concentrate the constraint resources in testing these weaker portions of the software. this study evaluates and compares the predictive capability of six machine learning algorithms amongst themselves and with logistic regression, a statistical algorithm for determining faulty portions of a software. the results are validated using seven open source software.

关键词： Fault Prediction Machine Learning Algorithms Object-Oriented Metrics

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From Reports to Bug-Fix Commits: A 10 Years Dataset of Bug-Fixing Activity from 55 Apache's Open Source Projects 15

From Reports to Bug-Fix Commits: A 10 Years Dataset of Bug-F...

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15th international conference on predictive models and Data Analytics in software engineering (promise)

作者： Vieira, Renan da Silva, Antonio Rocha, Lincoln Gomes, Joao Paulo Univ Fed Ceara Fortaleza Ceara Brazil

ISBN: (纸本)9781450372336

Bugs appear in almost any software development. Solving all or at least a large part of them requires a great deal of time, effort, and budget. software projects typically use issue tracking systems as a way to report and monitor bug-fixing tasks. In recent years, several researchers have been conducting bug tracking analysis to better understand the problem and thus provide means to reduce costs and improve the efficiency of the bug-fixing task. In this paper, we introduce a new dataset composed of more than 70,000 bug-fix reports from 10 years of bug-fixing activity of 55 projects from the Apache software Foundation, distributed in 9 categories. We have mined this information from Jira issue track system concerning two different perspectives of reports with closed/resolved status: static (the latest version of reports) and dynamic (the changes that have occurred in reports over time). We also extract information from the commits (if they exist) that fix such bugs from their respective version-control system (Git). We also provide a change analysis that occurs in the reports as a way of illustrating and characterizing the proposed dataset. Once the data extraction process is an error-prone nontrivial task, we believe such initiatives like this could be useful to support researchers in further more detailed investigations.

关键词： Bug-Fix Dataset Mining software Repositories software Traceability

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Using Ensembles for Class-Imbalance Problem to Predict Maintainability of Open Source software

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international JOURNAL OF RELIABILITY QUALITY AND SAFETY engineering 2020年第5期27卷

作者： Malhotra, Ruchika Lata, Kusum Delhi Technol Univ Discipline Software Engn Dept Comp Sci & Engn Bawana Rd New Delhi 110042 India Delhi Technol Univ Dept Comp Sci & Engn Bawana Rd New Delhi 110042 India Delhi Technol Univ Univ Sch Management & Entrepreneurship East Delhi CampusVivek Vihar Phase II New Delhi 110095 India

To facilitate software maintenance and save the maintenance cost, numerous machine learning (ML) techniques have been studied to predict the maintainability of software modules or classes. An abundant amount of effort has been put by the research community to develop software maintainability prediction (SMP) models by relating software metrics to the maintainability of modules or classes. When software classes demanding the high maintainability effort (HME) are less as compared to the low maintainability effort (LME) classes, the situation leads to imbalanced datasets for training the SMP models. the imbalanced class distribution in SMP datasets could be a dilemma for various ML techniques because, in the case of an imbalanced dataset, minority class instances are either misclassified by the ML techniques or get discarded as noise. the recent development in predictive modeling has ascertained that ensemble techniques can boost the performance of ML techniques by collating their predictions. Ensembles themselves do not solve the class-imbalance problem much. However, aggregation of ensemble techniques with the certain techniques to handle class-imbalance problem (e.g., data resampling) has led to several proposals in research. this paper evaluates the performance of ensembles for the class-imbalance in the domain of SMP. the ensembles for class-imbalance problem (ECIP) are the modification of ensembles which pre-process the imbalanced data using data resampling before the learning process. this study experimentally compares the performance of several ECIP using performance metrics Balance and g-Mean over eight Apache software datasets. the results of the study advocate that for imbalanced datasets, ECIP improves the performance of SMP models as compared to classic ensembles.

关键词： software maintainability prediction machine learning data resampling imbalanced learning

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Maximum Matching on Trees in the Online Preemptive and the Incremental Graph models 23rd

Maximum Matching on Trees in the Online Preemptive and the I...

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23rd international Computing and Combinatorics conference (COCOON)

作者： Tirodkar, Sumedh Vishwanathan, Sundar Tata Inst Fundamental Res Mumbai Maharashtra India Indian Inst Technol CSE Mumbai Maharashtra India

ISBN: (纸本)9783319623887

We study the Maximum Cardinality Matching (MCM) and the Maximum Weight Matching (MWM) problems, on trees and on some special classes of graphs, in the online preemptive and the incremental graph models. In the Online Preemptive model, the edges of a graph are revealed one by one and the algorithm is required to always maintain a valid matching. On seeing an edge, the algorithm has to either accept or reject the edge. If accepted, then the adjacent edges are discarded, and all rejections are permanent. In this model, the complexity of the problems is settled for deterministic algorithms (McGregor, in: Proceedings of the 8th international workshop on approximation, randomization and combinatorial optimization problems, and proceedings of the 9th international conference on randomization and computation: algorithms and techniques, APPROX'05/RANDOM'05, Springer, Berlin, pp. 170-181, 2005;Varadaraja, in: Automata, languages and programming: 38th international colloquium, ICALP 2011, Zurich, Switzerland, proceedings, part I, pp. 379-390, 2011. https://***/10.1007/978-3-642-22006-7_32). Epstein et al. (in: 30th international symposium on theoretical aspects of computer science, STACS 2013, Kiel, Germany, pp. 389-399, 2013. https://***/10.4230/***.2013.389) gave a 5.356-competitive randomized algorithm for MWM, and also proved a lower bound on the competitive ratio of (1+ln2)approximate to 1.693 for MCM. the same lower bound applies for MWM. In the Incremental Graph model, at each step an edge is added to the graph, and the algorithm is supposed to quickly update its current matching. Gupta (in: 34th international conference on foundation of software technology and theoretical computer science, FSTTCS 2014, 15-17 Dec 2014, New Delhi, India, pp. 227-239, 2014. https://***/10.4230/***.2014.227) proved that for any E <= 1/2, there exists an algorithm that maintains a (1+E)-approximate MCM for an incremental bipartite graph in an amortized update

关键词： Online preemptive model Incremental dynamic graph model Primal-dual analysis

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Study on a Novel Multiport Electric Energy Router for AC-DC Hybrid Micro-grid using Active NPC with Decentralized MPC Algorithm

Study on a Novel Multiport Electric Energy Router for AC-DC ...

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international conference on Power Electronics and Motion Control (IPEMC)

作者： Long Luo Yaohua Li Zixin Li Fanqiang Gao Institute of Electrical Engineering Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728153018

ISBN: (纸本)9781728153025

A 2 MVA three-port, i.e. 10 kV ac port, +5 kV dc port and ±1500 V dc port, electric energy router (EER) with single phase active ANPC structure of each terminal is introduced and developed. On-grid and islanded operation mode are discussed, in which decentralized model prediction control (MPC) method is mainly adopted. Compared with the traditional on- and off-grid research that only focuses on the control method of a single converter, this paper focuses on the overall coordinated control of each port. And comparative analysis between the traditional proportional integral (PI) control strategy and the MPC algorithm is conducted. the results indicate that the decentralized MPC performs better dynamic response which has a better effect on the realization of a fast on- and off-grid switching. And a novel on-grid and islanded mode phase synchronization unit is proposed. this synchronization unit uses the grid q-axis voltage to correct the phase of the EER to achieve stable synchronization. Simulation in Psim software shows decentralized MPC algorithm achieved better dynamic response while mode switching happened, and novel phase synchronization unit has a better continuity of the adjustment process compared with conventional method and synchronization parameters are easier to adjust.

关键词： Heuristic algorithms software algorithms Switches predictive models software Synchronization Reliability

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Road Traffic Risk Safety Prediction Based on BP Neural Network

Road Traffic Risk Safety Prediction Based on BP Neural Netwo...

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IEEE Joint international Information Technology and Artificial Intelligence conference (ITAIC)

作者： Zheng XIAO Ye TIAN Dongsheng CAO Ziling ZHANG School of Mechanical and Electronic Engineering Wuhan University of Technology P.R. China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

In the field of smart transportation, the prediction of road traffic accident is an essential part. It analyzes the collected traffic information and predict the occurrence of traffic accidents in order to achieve effectively early warning and reduce ultimately traffic accidents. Generally, the traffic information includes speed, weather, light and road conditions. these data are usually not quantified. thus, in this paper, those data will be quantified before they are able to be used as input parameters for the prediction. Based on data of historical traffic accidents and their statistical results in the UK, an accident severity model is established by regression analysis. A BP neural network optimized by genetic algorithm is utilized for predicting the accident severity under the current conditions. Via comparing both results, a road traffic safety risk prediction model is established. In order to improve the prediction accuracy of the BP neural network, three parameters for testing are presented and analyzed. Finally, the results show that the prediction of road traffic risk with optimized BP neural network is reasonable. It provides an effective safe driving for route planning.

关键词： Neural networks predictive models Road safety software Safety Planning Accidents

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Stability prediction of the software requirements specification

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software QUALITY JOURNAL 2018年第2期26卷 585-605页

作者： del Sagrado, Jose del Aguila, Isabel M. Univ Almeria Dept Informat Ctra Sacramento S-N Almeria 04120 Spain

Complex decision-making is a prominent aspect of Requirements engineering. this work presents the Bayesian network Requisites that predicts whether the requirements specification documents have to be revised. We test Requisites' suitability by means of metrics obtained from a large complex software project. Furthermore, this Bayesian network has been integrated into a software tool by defining a communication interface inside a multilayered architecture. In this way, we add a new decision-making functionality that provides requirements engineers with a feature to explore software requirement specification by combining requirement metrics and the probability values estimated by the Bayesian network.

关键词： Requirements engineering software requirements specification CASE tools Bayesian network

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software defect prediction: do different classifiers find the same defects?

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software QUALITY JOURNAL 2018年第2期26卷 525-552页

作者： Bowes, David Hall, Tracy Petric, Jean Univ Hertfordshire Sci & Technol Res Inst Hatfield AL10 9AB Herts England Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH Middx England

During the last 10 years, hundreds of different defect prediction models have been published. the performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Na < ve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. the defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.

关键词： software defect prediction Prediction modelling Machine learning

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Performance Prediction for Families of Data-Intensive software Applications 18

Performance Prediction for Families of Data-Intensive Softwa...

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9th ACM/SPEC international conference on Performance engineering (ICPE)

作者： Verriet, Jacques Dankers, Reinier Somers, Lou TNO ESI Eindhoven Netherlands Oce Technol BV Venlo Netherlands

ISBN: (纸本)9781450356299

Performance is a critical system property of any system, in particular of data-intensive systems, such as image processing systems. We describe a performance engineering method for families of data-intensive systems that is both simple and accurate;the performance of new family members is predicted using models of existing family members. the predictive models are calibrated using static code analysis and regression. Code analysis is used to extract performance profiles, which are used in combination with regression to derive predictive performance models. A case study presents the application for an industrial image processing case, which revealed as benefits the easy application and identification of code performance optimization points.

关键词： software performance engineering loop analysis product families data-intensive systems

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promise 2013: the 9th international conference on predictive models in software engineering

PROMISE 2013: The 9th international conference on predictive...

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2013 ACM / IEEE international Symposium on Empirical software engineering and Measurement, ESEM 2013

作者： Turhan, Burak Wagner, Stefan Bener, Ayse Di Penta, Massimiliano Yang, Ye Dept. of Information Proc. Science University of Oulu Oulu Finland Institute of Software Technology University of Stuttgart Stuttgart Germany Department of Mechanical and Industrial Engineering Ryerson University Toronto ON Canada Department of Engineering University of Sannio Benevento Italy Institute of Software Chinese Academy of Sciences Beijing China

promise conference is an annual forum for researchers and practitioners to present, discuss and exchange ideas, results, expertise and experiences in construction and/or application of prediction models in software engineering. Such models could be targeted at: planning, design, implementation, testing, maintenance, quality assurance, evaluation, process improvement, management, decision making, and risk assessment in software and systems development. promise is distinguished from similar forums with its public data repository and focus on methodological details, providing a unique interdisciplinary venue for software engineering and machine learning communities, and seeking for verifiable and repeatable prediction models that are useful in practice. © 2013 IEEE.

关键词： Risk assessment

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