مقاله انگلیسی رایگان در مورد فعالیتهای نرم افزاری کاوش اتوماتیک – IEEE 2019

IEEE

 

مشخصات مقاله
ترجمه عنوان مقاله فعالیتهای روند نرم افزاری کاوش اتوماتیک در زمان واقعی از لاگ های کنترل نسخه تخریبی (SVN) با استفاده از طبقه بندی کننده نایو بیز (Bayes Naive)
عنوان انگلیسی مقاله Automatic Real-Time Mining Software Process Activities From SVN Logs Using a Naive Bayes Classifier
انتشار مقاله سال ۲۰۱۹
تعداد صفحات مقاله انگلیسی ۱۳ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه IEEE
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) Scopus – Master Journals List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۴٫۶۴۱ در سال ۲۰۱۸
شاخص H_index ۵۶ در سال ۲۰۱۹
شاخص SJR ۰٫۶۰۹ در سال ۲۰۱۸
شناسه ISSN ۲۱۶۹-۳۵۳۶
شاخص Quartile (چارک) Q2 در سال ۲۰۱۸
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر ندارد
رفرنس دارد
رشته های مرتبط مهندسی کامپیوتر
گرایش های مرتبط مهندسی نرم افزار، هوش مصنوعی
نوع ارائه مقاله
ژورنال
مجله / کنفرانس دسترسی – IEEE Access
دانشگاه  School of Software, Yunnan University, Kunming 650091, China
کلمات کلیدی طبقه بندی کننده فعالیت، یادگیری ماشین، فعالیت روند نرم افزاری، لاگ کنترل نسخه تخریبی (SVN)
کلمات کلیدی انگلیسی  Activity classifier, machine learning, software process activity, SVN log
شناسه دیجیتال – doi
https://doi.org/10.1109/ACCESS.2019.2945608
کد محصول  E13852
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
I. Introduction
II. Background
III. General Framework
IV. Activity Extraction From the SVN Log Based on Semantic Features
V. Dynamic Incremental Method of Event-Activity Mapping
Authors
Figures
References

 

بخشی از متن مقاله:
Abstract

The abundance of event data in current software configuration management systems makes it possible to discover software process models automatically by using actual observed behavior. However, traditional process mining algorithms cannot be applied to event logs recorded in software configuration management (SCM) systems, such as SVN, because of missing activity attributes. To address this problem, a software process activity classifier is proposed to build event-activity mapping relationships from software development event streams, revealing activity attributes and associating the activity to the original SVN log. The proposed approach extracts activity from the SVN log based on semantic features and introduces a novel technique based on a naive Bayes approach to associate event activities dynamically. The approach has been applied to two real-world software development process logs, ArgoUML and jEdit, consisting of more than 80,000 events, covering development information from 1998 to 2015. With the application of our approach to such data, activities can be extracted from event logs and a classifier can be constructed for adding activity attributes to new events. The results of the classification are evaluated in terms of precision rate, recall rate, and the F-measure. Overall, two real-world software development process logs are used to validate the method, and the experimental results show that the approach can mine software process activities from SVN log events automatically and in real-time.

Introduction

Nowadays, it is widely accepted that the quality of software is not only related to the product, but to the organization and to the production process that is carried out [1]–[۳]. Software process modeling helps to create process descriptions that correspond to processes actually performed during software development or maintenance. Process models can be used to visualize tacit knowledge, roles, and information flows in the processes, identifying points for improvement and optimization [4], [5]. However, with the deepening of research on software development, problems associated with traditional subjective modeling methods have become apparent. These arise because the task of designing a software process model is complex and error prone, and because the life cycle of the model is short, individuals are not sensitive to differences between actual processes and the process model, and there are increasing requirements for process engineers, all while the software process is still evolving [6]–[۸]. As software systems become more and more complex, the establishment of a sound process model is becoming more like ‘‘an art rather than a science’’ [۹].

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