A Survey on Bug Triage Using Data Reduction Technique

Most of the software companies needs to deal with software bug in every day. Software companies spend most if their cost in dealing with software bugs. The process of fixing bug is bug triage, which aims to assign a expert developer to a new bug. To reduce the time and cost in manual work, we apply text classification technique to conduct automatic bug triage. In proposed system we apply data reduction techniques on bug data set to improve the scale and quality of bug data. We use instance selection and feature selection simultaneously to reduce the scales on bug dimension and word dimension and improve the accuracy of bug triage. In this paper, we investigate the use of five term selection methods on the accuracy of bug assignment. In addition, we re-balance the load between developers based on their experience

Framework for Automatic Bug Classification in Bug Triage System

A Software bug is an error, flaw, failure or fault in a computer program or system that causes it to produce an incorrect or unexpected result. When bugs arise, we have to fix them which is not easy. Most of the companies spend 40% of cost to fixing bugs. The process of fixing bug is bug triage or bug assortment. Triagingthis incoming report manually is error prone and time consuming .Software companies spend most of their cost in dealing with these bugs. In this paper we classifying the bugs so that we can determine the class of the bug at which class that bug is belongs and after applying the classification we can assign the particular bug to the exact developer for fixing them. This is efficient. In this paper we are using combination of two classification techniques ,na�ve Bayes (NB) and k nearest neighbor(KNN).In modern days company uses automatic bug triaging system but in Traditional manual Triaging system is used which is not efficient and taking too much time .For triaging the bug we require bug detail which is called bug repository. In this paper we also reducing the bug dataset because if we having more data with unused information which causes problem to assigning bugs. For implementing this we use instance selection and feature selection for reducing bug data. This paper describe the whole procedure of bug allotment from starting to end and at last result will show on the basis of graph .Graph represents the maximum possibility of class means at which class the bug will belongs

Finding Relevant Answers in Software Forums

Abstract—Online software forums provide a huge amount of valuable content. Developers and users often ask questions and receive answers from such forums. The availability of a vast amount of thread discussions in forums provides ample opportunities for knowledge acquisition and summarization. For a given search query, current search engines use traditional information retrieval approach to extract webpages containin

Duplicate bug report detection using clustering

Bug reporting and fixing the reported bugs play a critical part in the development and maintenance of software systems. The software developers and end users can collaborate in this process to improve the reliability of software systems. Various end users report the defects they have found in the software and how these bugs affect them. However, the same defect may be reported independently by several users leading to a significant number of duplicate bug reports. There are a number of existing methods for detecting duplicate bug reports, but the best results so far account for only 24% of actual duplicates. In this paper, we propose a new method based on clustering to identify a larger proportion of duplicate bug reports while keeping the false positives of misidentified non-duplicates low. The proposed approach is experimentally evaluated on a large sample of bug reports from three public domain data sets. The results show that this approach achieves better performance in terms of a harmonic measure that combines true positive and true negative rates when compared to the existing methods

Product assignment recommender

Effectiveness of software development process depends on the accuracy of data in supporting tools. In particular, a customer issue assigned to a wrong product team takes much longer to resolve (negatively affecting user-perceived quality) and wastes developer effort. In Open Source Software (OSS) and in commercial projects values in issue-tracking systems (ITS) or Customer Relationship Management (CRM) sys-tems are often assigned by non-developers for whom the assignment task is difficult. We propose PAR (Product As-signment Recommender) to estimate the odds that a value in the ITS is incorrect. PAR learns from the past activities in ITS and performs prediction using a logistic regression model. Our demonstrations show how PAR helps develop-ers to focus on fixing real problems, and how it can be used to improve data accuracy in ITS by crowd-sourcing non-developers to verify and correct low-accuracy data. Copyright ? 2014 ACM.EI

Impact of Triage: A Study of Mozilla and Gnome

Abstract—Triage is of great interest in software projects because it has the potential to reduce developer effort by involving a broader base of non-developer contributors to filter and augment reported issues. Using issue tracking data and interviews with experienced contributors we investigate ways to quantify the impact of triagers on reducing the number of issues developers need to resolve in two OSS projects: Mozilla and Gnome. We find the primary impact of triagers to involve issue filtering, filling missing information, and determining the relevant product. While triagers were good at filtering invalid issues and as accurate as developers in filling in missing issue attributes, they had more difficulty accurately pinpointing the relevant product. We expect that this work will highlight the importance of issue triage in software projects and will help design further studies on understanding and improving triage practices. I