Extreme programming: a university team design experience
Bibliographic record
Abstract
The paper discusses an experience in applying the extreme programming approach to the 4 year team design project course. Extreme programming is a methodology for software system development that focuses on high customer integration, extensive testing, code-centered development and documentation, refactoring and paired programming. Typically, the project course is managed using the standard waterfall or V-shaped development models with a faculty advisor acting as a customer for the project. In this project extreme programming has been used instead. Extreme programming is based on a sequence of development practices, including pair programming, very accurate configuration management, strong customer interaction based on "system stories", detailed testing. In this project, paired programmers are used for the duration of a release and then the pairs rotate. The distributed programming environment is handled using the JCVS suite of configuration management tools. Every 3-4 weeks, a new fully functional release is delivered and reviewed by the customer. The specifications for each release are captured incrementally using use case scenarios. Only the essential requirements for the current iteration are implemented. The JUnit test suite is also used to test each of the Java classes on an ongoing basis. The test suite verifies all aspects of the software at each build; this is necessary when refactoring components. Requirements capture, design and implementation of the deliverables are performed incrementally and result in quicker development times and reduced defects. Refactoring is applied wherever possible to simplify the code. Documentation is applied using the standard JavaDoc utility and is kept to a minimum. Finally, customer feedback is immediately incorporated into future iterations of the design process.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".