Requirements Engineering and Management Effects on Downstream Developer Performance in a Small Business Findings from a Case Study in a CMMI/CMM Context
Bibliographic record
Abstract
Abstract— This thesis is a case study explaining how I tried to improve the requirements engineering process at company X (not its real name), a small software development company in Waterloo, ON, Canada. I assessed X’s practices and standards using the Capability Maturity Model Integration (CMMI) and the Requirements Definition and Management (RDM) Maturity Model (RDMMM). I chose CMMI because it defines and measures a company to assess its maturity as an organization. Higher levels of CMMI have been found to have a correlation with the better success of projects, with regards to delivering the product on-time, on-budget, and on function. For analysis, initial measurements of the company’s performance were gathered to compare results in order to measure X’s process improvements. Six common performance metrics were analyzed: error density, software development productivity, percentage of rework, cycle time for the completion of a typical software project, schedule fidelity, and error detection effectiveness. In addition, I gave a questionnaire to X’s employees based on Ellis’s RDM which is a process for defining, documenting, and maintaining documents that take its reference point from empirical studies on the effectiveness of CMMI. [19] This case study’s survey questions were used to elicit the data necessary to answer whether higher levels of the RDMMM in strategic projects lead to the success of projects at X. The different levels of RDMMM within the company were measured by comparing the questionnaire results taken in 2017 and 2019. Many of the conclusions and the results of this paper are based on the interviews and personal statements from employees at X about their experience in software development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".