On requirements elicitation for Software Projects in ICT for development
Bibliographic record
Abstract
Currently, there is much interest in harnessing the potential of new and affordable Information and Communication Technologies (ICT) such as mobile phones, to assist in reducing disparities in socioeconomic conditions throughout the world. Such efforts have come to be known as ICT for Development or ICT4D. While this field of research holds much promise, few projects have managed to achieve long-term sustained success. Among the many reasons for this, from a software engineering perspective, in many cases it can be attributed to inadequacies in the gathering and defining of software requirements. Failures in realising sustainable systems stern from inadequate consideration of the high-level socioeconomic development goals, neglect of environmental constraints, and a lack of adequate input from end-users regarding their specific needs and sociocultural context. The situation is exacerbated by inadequate reporting on the social impact of such interventions, making it difficult to assess a project's success, let alone apply lessons learned to new projects. In this thesis we propose enhancing conventional requirements elicitation with a complementary elicitation methodology specifically adapted to address these shortcomings. Our approach is based on a proposed novel technique of Structured Digital Storytelling to elicit input from end-users having limited literacy in the form of stories. The proposed methodology includes a systematic method for extracting and interpreting the informational content of the stories that applies a conceptual model derived from Communications Theory to identify constraints arising from the users' sociocultural context. The thesis introduces an ICT4D quality model identifying non-functional requirements related to the sociodynamics of a system's sustained use in a rural community. The needs, goals and constraints thus identified are integrated using a goal-based analysis to produce a more informed understanding of potential areas of technology intervention and to develop high-level functional and non-functional software requirements. The resulting goal model is also used in deriving a measurement framework for assessing a project's success based on its social impact. We illustrate our approach and validate its effectiveness with a field study. Keywords: ICT4D, digital divide. requirements engineering. needs elicitation, requirements elicitation, culture, storytelling
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 | 0.045 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".