scholarly journals A Bayesian Belief Network for Modeling Open Source Software Maintenance Productivity

2016 ◽  
pp. 32-44 ◽  
Author(s):  
Stamatia Bibi ◽  
Apostolos Ampatzoglou ◽  
Ioannis Stamelos
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Maintenance ◽  
Bayesian Belief Network ◽  
Belief Network

AN EMBEDDED OSS RELIABILITY AND OPTIMIZATION ANALYSIS INCORPORATING IMPERFECT DEBUGGING

2009 ◽  
Vol 16 (04) ◽  
pp. 371-384
Author(s):  
YOSHINOBU TAMURA ◽  
SHIGERU YAMADA
Keyword(s):  
Software Development ◽  
Open Source ◽  
Open Source Software ◽  
Software Reliability ◽  
Software Maintenance ◽  
Reliability Assessment ◽  
Release Time ◽  
Network Computing ◽  
Imperfect Debugging ◽  
Network Technologies

As a result of the technological progress, software development environment has changed into development paradigm based on client/server systems by using network computing technologies. Network technologies have made rapid progress with the dissemination of computer systems in all areas. These network technologies become increasingly more complex in a wide sphere. Especially, open source software systems which serve as key components of critical infrastructures in the society are still ever-expanding now. In this paper, we propose a method of software reliability assessment based on stochastic differential equations. Especially, we derive several assessment measures in terms of imperfect debugging. Also, we analyze actual software fault-count data to show numerical examples of software reliability assessment for an embedded open source software. Further, it has been necessary to manage the software development process in terms of reliability, effort, and release time. Then, we find the optimal release time based on the total expected software maintenance effort.

scholarly journals Impact of Historical Software Metric Changes in Predicting Future Maintainability Trends in Open-Source Software Development

2020 ◽  
Vol 10 (13) ◽  
pp. 4624
Author(s):  
Mitja Gradišnik ◽  
Tina Beranič ◽  
Sašo Karakatič
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Maintenance ◽  
Software Metrics ◽  
Prediction Models ◽  
Measurement Data ◽  
Software Project ◽  
Software Projects ◽  
Software Metric ◽  
Software Maintainability

Software maintenance is one of the key stages in the software lifecycle and it includes a variety of activities that consume the significant portion of the costs of a software project. Previous research suggest that future software maintainability can be predicted, based on various source code aspects, but most of the research focuses on the prediction based on the present state of the code and ignores its history. While taking the history into account in software maintainability prediction seems intuitive, the research empirically testing this has not been done, and is the main goal of this paper. This paper empirically evaluates the contribution of historical measurements of the Chidamber & Kemerer (C&K) software metrics to software maintainability prediction models. The main contribution of the paper is the building of the prediction models with classification and regression trees and random forest learners in iterations by adding historical measurement data extracted from previous releases gradually. The maintainability prediction models were built based on software metric measurements obtained from real-world open-source software projects. The analysis of the results show that an additional amount of historical metric measurements contributes to the maintainability prediction. Additionally, the study evaluates the contribution of individual C&K software metrics on the performance of maintainability prediction models.

scholarly journals MULTI-FEATURE MODEL FOR DUPLICATE BUG REPORTS DETECTION SOLUTION

2019 ◽  
Vol 1 (1) ◽  
pp. 29-36
Author(s):  
Phuc Minh Nhan ◽  
Thien Hoang Duy Nguyen
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Maintenance ◽  
Feature Model ◽  
Software Bugs ◽  
Bug Reports ◽  
Duplicate Bug Reports ◽  
Error Reports ◽  
Bug Management

For open source software such as Firefox, Eclipse, Subversion,. . . they usually have a system for bug management that sent by users. These bug reports help the system identify various software bugs which makes software maintenance better. However, a situation occurs that there are many error reports sent to  the processing repository that these bug reports were previously reported by different users, this is called duplicate bug reports. In this paper, we introduce a multi-feature model combined with weighted improvements from CFC (ClassFeature-Centroid) to detect duplicate bug reportsautomatically. We have experimented on three projects of Mozilla, Eclipse and OpenOffice. The results show that our method can improve 8-12% better as compared to the compared methods.

scholarly journals Mining Severe Priority Bugs in Software Maintenance

2019 ◽  
Vol 8 (4S2) ◽  
pp. 729-732
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Maintenance ◽  
Maintenance Phase ◽  
Bug Reports ◽  
Bug Tracking ◽  
The Right ◽  
Software Bug ◽  
Positive Results

Maintenance of open source software is a hectic task as the number of bugs reported is huge. The number of projects, components and versions in an open source project also contribute to the number of bugs that are being reported. Classification of bugs based on priority and identification of the suitable engineers for assignment of bugs for such huge systems still remains a major challenge. Bugs that are misclassified or assigned to engineers who don’t have the component expertise, drastically affect the time taken towards bug resolution. In this paper we have explored the usage of data mining techniques on the classification of bugs and assignment of bugs to engineers.Our focus was on classifying bugs as either severe or non-severe and identification of engineers who have the right expertise to fix the bugs. The prediction of bug severity and identification of engineers were done by mining bug reports from JIRA, an open source software bug tracking tool. The mining process yielded positive results and will be a decision enhancer for severe bugs in the maintenance phase

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