Software Process Improvement for Small and Very Small Enterprises
Bibliographic record
Abstract
Software organizations have been struggling for decades to improve the quality of their products by improving their software development processes. Designing an improvement program for a software development process is a demanding and complex task. This task consists of two main processes: the assessment process and the improvement process. A successful improvement process requires first a successful assessment; failing to assess the organization’s software development process could create unsatisfactory results. Although very small enterprises (VSEs) have several interesting characteristics such as flexibility and ease of communications, initiating an assessment and improvement process based on well-known Software Process Improvement (SPI) models such as Capability Maturity Model Integration (CMMI) and ISO 15504 is more challenging in such VSEs. Accordingly, researchers and practitioners have designed a few assessment methods to meet the needs of VSEs organizations to initiate an SPI process. This chapter discusses the assessment and improvement process in VSEs; we first examine VSEs characteristics and problems. Next, we discuss the different assessment methods and standards designed to fit the needs of such organizations and how to compare them. Finally, we present future research work perceived in this context.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".