Proceedings of the Second International Workshop on Context for Software Development
Bibliographic record
Abstract
It is our pleasure to welcome you to the (pre-workshop) proceedings of the 2nd International Workshop on Context for Software Development (CSD 2015) co-located with the 37th International Conference on Software Engineering (ICSE 2015) to be held in Florence, Italy on May 19th, 2015. There is a great deal of context that is needed for a developer to fully understand a task, including relevant software artifacts and their change history, requirements, design specifications, dependent tasks, concurrent work, discussions and knowledge exchanges about those tasks and artifacts, and more. In fact, context in software development is multifaceted, and what information is relevant as context for a developer working on a given task is not fully understood. Developers must make use of knowledge gleaned from all of this context to make decisions, coordinate their work, understand the purpose behind their tasks, and understand how their tasks fit with the rest of the project. However, there is little research on what type of context is needed for a developer to complete a task, how we can model context around a task, and how we can use those models in software development at large. Identifying and modeling context in software development will lay the foundation for future software engineering techniques and tools that leverage development context for better support of software developers as they manage and make use of the copious amount of context around their development tasks. Context is also important for empirical software engineering research since the software development process is dependent on many factors of the development setting and these factors are important to understanding results of research studies. The goal of the workshop is to bring together researchers interested in developing a better understanding of the context needed for software development. At the workshop, we will discuss: •the types of context needed to successfully complete a development task •how to model context around a task •techniques and tools that leverage context information around development activities for better support of software development activities
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.049 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".