Requirements Engineering Quality Revealed through Functional Size Measurement: An Empirical Study in an Agile Context
Bibliographic record
Abstract
Software development organizations applying continuous process improvement, when faced with the limits of qualitative approaches, are looking into quantitative approaches to support decision making, namely for improvement of the software project estimation process. Quantitative approaches include sizing functional requirements with standards such as ISO 19761, known as the COSMIC method. But defects in the requirements may have an impact on the accuracy of the resulting functional size, as well as an impact on the project relative effort sometimes known as the 'productivity rate' and the measurement relative effort. Our research program is investigating the relationship between the attributes of requirements engineering (RE) outputs, the software process relative effort, and the measurement process relative effort. RE outputs studied are requirements and specifications documents and data models. As functional sizing is applied, thorough examination of RE outputs is done, which is likely to lead to identifying quality attributes and related findings. As a case study, this paper reports preliminary results related to the quality of requirements artefacts from a software development organization that is applying the Agile approach to its software development process. The functional size of the software developed through five projects was measured and compared with development effort and measurement effort, taking into account the quality rating of requirements. The results led to recommendations of improvement on the RE process that the organization could deploy in its current and next software projects. This paper also presents a list of functional sizing challenges that the measurer has faced, leading to proposed recommendations for planning any software measurement project.
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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 | 0.017 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".