Transitioning to Agile: A Framework for Pre-adoption Analysis using Empirical Knowledge and Strategic Modeling
Bibliographic record
Abstract
Transitioning to the Agile style of software development has become an increasing phenomenon among software companies. The commonly perceived advantages of Agile, such as shortened time to market, improved efficiency, and reduced development waste are among key driving motivations of organizations to Agile. Each year a considerable number of empirical studies are being published, reporting on successful or unfavorable outcomes of enacting Agile in various organizations. Reusing this body of knowledge, and turning it into a concise and reachable source of information on Agile practices, can help many software organizations which are at the edge of transition to Agile, dealing with the uncertainties of such a decision. \nOne of the early steps of transitioning to Agile (or any other process model) is to confirm the adaptability of new process with the current organization. Various Agile adoption frameworks have proposed different checklists to test the readiness of an organization for becoming Agile, or to identify the required adaptation criteria. Transitioning to Agile, as a significant organizational initiative, is a strategic decision, which should be made with respect to key objectives of the target organization. Having a reliable anticipation of how a new process model will impact the strategic objectives helps organizational managers to choose a process model, which brings optimum advantage to the organization. \nThis thesis introduces a framework for evaluating new Agile practices (compartments of Agile methods) prior to their adoption in an organization. The framework has two distinguishing characteristics: first, it acts strategically, as it puts the strategic model of organization at the center of many decision makings that should be performed during Agile adoption; and second, it is based on a repository of Agile practices that allows the framework to benefit from the empirical knowledge of Agile methods, in order to improve the reliability of its outcomes. This repository has been populated through an extensive literature review of empirical studies on Agile methods. \nThe framework was put in practice in an industrial case, at one of the R&D units of Ericsson Company in Italy. The target R&D unit was proposed with a number of Agile practices. The application of framework helped R&D unit managers to strategically decide on the new process proposal, by having a better understanding of its strategic shortcomings and strengths. A key portion of frameworkâs analysis results were evaluated one year after the R&D unit made the transition to Agile, showing that over 75% of pre-adoption analysis results came to reality after the enactment of new process into the organization.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".