On the many ways software engineering can benefit from knowledge engineering
Bibliographic record
Abstract
Software Engineering is not only a technical discipline of its own. It is also a problem domain where technologies coming from other disciplines are relevant and can play an important role. One important example is knowledge engineering, a term that I use in the broad sense to encompass artificial intelligence, computational intelligence, knowledge bases, data mining, and machine learning. I see a number of typical software development issues that can benefit from these disciplines and, for the sake of clarifying the discussion, I have divided them into four categories: (1) Planning, monitoring, and quality control of projects, (2) The quality and process improvement of software organizations, (3) Decision making support, (4) Automation.First, the planning, monitoring, and quality control of software development is typically based, unless it is entirely ad-hoc, on past project data and/or expert opinion. As discussed below, several techniques coming from machine learning, computational intelligence, and knowledge-based systems have shown to be useful in this context. Second, software organizations are inherently learning organizations, that need to improve, based on experience and project feedback, the way they develop software in changing and volatile environments. Large amounts of data, numerous documents, and other forms of information are typically gathered on projects. The question then becomes how to enable the intelligent storage and use of such information in future projects. Third, during the course of a project, software engineers and managers have to face important, complex decisions. They need decision models to support them, especially when project pressure is intense. Techniques originally developed for building risk models based on expert elicitation or optimization heuristics can play a key role in such a context. The last category of applications concerns automation. Many automation problems, such as test data generation, can be formulated as constraint solving problems. A number of metaheuristic algorithms can be adapted for that purpose and have shown to be practically usable and flexible to adjust to numerous situations.This paper discusses all the points above, identify open issues and future research directions, and provide some carefully selected, key pointers for further reading.
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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.026 | 0.065 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".