Dear professor Michael Scott Brown

Finding a specific dataset that directly provides the level of effort to resolve software defects can be challenging. However, you can explore alternative datasets and research studies that may offer insights into software defect resolution effort. Here are some potential resources to consider:

1. SEACRAFT dataset: The SEACRAFT (Software Engineering Artifact Curation) project collects and curates datasets related to software engineering research. While it may not have a specific dataset dedicated to software defect resolution effort, it provides various software engineering datasets that can be used for related analyses. You can explore their website and publications for relevant datasets and research insights.

2. Research papers: Explore research papers in the field of software engineering, defect prediction, and software metrics. Many researchers propose models and techniques for estimating software defect resolution effort. By studying these papers, you can gather insights into methodologies, features, and datasets used in such studies. IEEE Xplore, ACM Digital Library, and Google Scholar are good starting points for finding relevant research papers.

3. Collaborative platforms: Check collaborative platforms like GitHub, GitLab, or Bitbucket, where developers contribute to open-source projects. While these platforms may not explicitly provide effort estimation data, you can find repositories containing issues and pull requests with discussions and timestamps. Analyzing the discussions and timestamps can provide some indications of the resolution effort involved.

4. Industry reports and case studies: Look for industry reports and case studies that discuss software defect resolution efforts. These reports may provide qualitative insights into the challenges, practices, and approximate effort required to fix different types of defects. Websites of software consulting companies, industry conferences, and software engineering forums can be valuable sources for such reports.

Remember that finding a dataset specifically focused on software defect resolution effort may be difficult, but by exploring related resources, research papers, and industry insights, you can gather information and develop predictive models for estimating effort based on available data.

References:

SEACRAFT project: http://www.seacraft.de/projects.html

IEEE Xplore: https://ieeexplore.ieee.org/

ACM Digital Library: https://dl.acm.org/

GitHub: https://github.com/

GitLab: https://about.gitlab.com/

Bitbucket: https://bitbucket.org/

Software engineering consulting companies, industry conferences, and software engineering forums can be found through online search engines and community platforms.