فعالیتهای نرم افزاری کاوش اتوماتیک
ترجمه نشده

فعالیتهای نرم افزاری کاوش اتوماتیک

عنوان فارسی 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
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی نرم افزار، هوش مصنوعی
کلمات کلیدی فارسی: 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
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2945608
دانشگاه: School of Software, Yunnan University, Kunming 650091, China
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 13
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13852
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

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

نمونه متن انگلیسی 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]–[3]. 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]–[8]. 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’’ [9].

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