A lightweight and flexible process for designing intuitive error handling and effective error messages
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unintuitive error handling and ineffective messages result in lost revenue, wasted time, and unsatisfied customers. Yet, error conditions are often considered edge cases. As a result, a focus on error conditions is usually left until late in the software design and development cycle and is sometimes limited to just resolving unexpected test case failures. This paper outlines a process that enables software development teams to collaborate more effectively to produce intuitive error handling and useful error messages. The process is structured: there are artifacts to produce and rituals to step through. However, the process is also lightweight and flexible. In addition, the process scales well for small and large projects. This paper also describes the benefits that the IBM® DB2® for Linux®, UNIX®, and Windows® (henceforth referred to as IBM DB2 LUW) software development team discovered after adopting this process, including better quality messages, a shorter and easier-to-manage translation cycle, and improved integration between error messages and related product documentation.
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.
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 | 0.001 | 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 it