Bibliographic record
Abstract
Context-aware application development frameworks enable context management and environment adaptation to automatize people’s activities. New technologies such as 5G and the Internet of Things (IoT) increase environment context (from devices/services), making functionalities available to augment context-aware applications. The result is an increased deployment of context-aware applications to support end-users in everyday activities. However, developing applications in context-aware frameworks involve diverse technologies, so that it traditionally involves software experts. In general, context-aware applications are limited in terms of personalization for end-users. They include configurations to personalize applications, but non-software experts can only change some of these configurations. Nowadays, advances in human–computer interaction provide techniques/metaphors to approach non-software experts. One approach is end-user development (EUD)—a set of activities and development tools that considers non-software experts as application builders. In this paper, we present our analysis of existing EUD approaches for building context-aware applications. We present a literature review of 37 screened papers obtained from research databases. This review aims to identify the methods, techniques, and tools proposed to build context-aware applications. Specifically, we reviewed EUD building techniques and implementations. Building techniques include metaphors/interaction styles proposed for application specification, composition, and testing. The implementations include a specification method to integrate and process context on the target application platforms. We also present the adoption trend and challenges of context-aware end-user development.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".