Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 62 tok/s
Gemini 2.5 Pro 48 tok/s Pro
GPT-5 Medium 14 tok/s Pro
GPT-5 High 13 tok/s Pro
GPT-4o 93 tok/s Pro
Kimi K2 213 tok/s Pro
GPT OSS 120B 458 tok/s Pro
Claude Sonnet 4 38 tok/s Pro
2000 character limit reached

Assessing the Fault Proneness Degree (DFP) by Estimating the Impact of Change Request Artifacts Correlation (1502.00695v1)

Published 3 Feb 2015 in cs.SE

Abstract: Exploring the impact of change requests applied to a software maintenance project helps to assess the fault-proneness of the change request to be handled further, which is perhaps a bug fix or even a new feature demand. In practice, the major development community stores change requests and related data using bug tracking systems such as Bugzilla. These data, together with the data stored in a versioning system, such as Concurrent Versioning Systems, are a valuable source of information to create descriptions and also can perform useful analyzes. In our earlier work, we proposed a novel statistical bipartite weighted graph-based approach to assessing the degree of fault-proneness of the change request and Change Request artifacts. With the motivation gained from this model, here we propose a novel strategy that estimates the degree of fault-proneness of a change request by assessing the impact of a change request artifact towards fault-proneness that considers the correlation between change requests artifact as another factor, which is in addition to our earlier strategy. The proposed model can be titled as Assessing the Fault Proneness Degree of Change Request Artifacts by estimating the impact of change requests correlation (DFP-CRC). As stated in our earlier model, the method DFP-CRC also makes use of information retrieval methods to identify the change request artifacts of the devised change request. And further evaluates the degree of fault-proneness of the Change Requests by estimating the correlation between change requests. The proposed method is evaluated by applying on concurrent versioning and Change request logs of the production level maintenance project.

Citations (1)

Summary

We haven't generated a summary for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Lightbulb On Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube