Survey on Automated Bugs Triage System

Nowadays IT companies is spending more than 40 percent of their cost in fixing software bugs, traditonally these bugs are fixed by manual assignement to a particular developer , this approach causes too much dependency, the new and alternative approach is the bug triage system which fix the bug automatically , which automatically assign the reported bug to a develop which decreases the time and cost in in manual work, different classification techniques are used to conduct automatic bug triage. In this paper, we propose to apply machine learning techniques to assist in bug triage to predict which developer should be assigned on the bug based on its description by applying text categrorization . We will address the problem of data reduction for bug triage, i.e. How the quality of bug data would be improved

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

Effective Bug Assortment Using Data Reduction Techniques

Software companies spend over 48% of their total cost to fix the bugs. An effective way to automatically fix the bugs to the correct developer is called Bug Triage or Bug Assortment. Data sets containing the bug reports are collected from two large open source projects like Mozilla and Firefox. These projects consist of open source bug repositories. Bug repositories are large repositories which stores all the details of bugs. The details are stored in the form of a bug report. These bug report are saved as a document and a related developer is mapped to the label of the document. Software companies spend most of their total cost in fixing these bugs. In bug repositories the two main challenges faced is the large quantity of the data set and the low quality. Noise and redundancy are the main cause for the low quality of the data set. However, irrespective of all these difficulties assigning a proper developer to fix the bug is not an easy task without knowing the actual class of the bug. In this paper we propose data reduction technique which reduces the high scale of the data but it retains the quality of the data set. We also propose domain wise bug solution

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

PREM: Prestige Network Enhanced Developer-Task Matching for Crowdsourced Software Development

Many software organizations are turning to employ crowdsourcing to augment their software production. For current practice of crowdsourcing, it is common to see a mass number of tasks posted on software crowdsourcing platforms, with little guidance for task selection. Considering that crowd developers may vary greatly in expertise, inappropriate developer-task matching will harm the quality of the deliverables. It is also not time-efficient for developers to discover their most appropriate tasks from vast open call requests. We propose an approach called PREM, aiming to appropriately match between developers and tasks. PREM automatically learns from the developers’ historical task data. In addition to task preference, PREM considers the competition nature of crowdsourcing by constructing developers’ prestige network. This differs our approach from previous developer recommendation methods that are based on task and/or individual features. Experiments are conducted on 3 TopCoder datasets with 9,191 tasks in total. Our experimental results show that reasonable accuracies are achievable (63%, 46%, 36% for the 3 datasets respectively, when matching 5 developers to each task) and the constructed prestige network can help improve the matching results

Effective Bug Triage based on Historical Bug-Fix Information

International audienceFor complex and popular software, project teams could receive a large number of bug reports. It is often tedious and costly to manually assign these bug reports to developers who have the expertise to fix the bugs. Many bug triage techniques have been proposed to automate this process. In this pa-per, we describe our study on applying conventional bug triage techniques to projects of different sizes. We find that the effectiveness of a bug triage technique largely depends on the size of a project team (measured in terms of the number of developers). The conventional bug triage methods become less effective when the number of developers increases. To further improve the effectiveness of bug triage for large projects, we propose a novel recommendation method called BugFixer, which recommends developers for a new bug report based on historical bug-fix in-formation. BugFixer constructs a Developer-Component-Bug (DCB) network, which models the relationship between developers and source code components, as well as the relationship be-tween the components and their associated bugs. A DCB network captures the knowledge of "who fixed what, where". For a new bug report, BugFixer uses a DCB network to recommend to triager a list of suitable developers who could fix this bug. We evaluate BugFixer on three large-scale open source projects and two smaller industrial projects. The experimental results show that the proposed method outperforms the existing methods for large projects and achieves comparable performance for small projects