Inferring visual contracts from Java programs
- Submitting institution
-
The University of Leicester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2389
- Type
- D - Journal article
- DOI
-
10.1007/s10515-018-0242-9
- Title of journal
- Automated Software Engineering
- Article number
- -
- First page
- 745
- Volume
- 25
- Issue
- 4
- ISSN
- 0928-8910
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
https://doi.org/10.1007/s10515-018-0242-9
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- We derive high-level behavioural rules from changing data, here observations of Java executions. Unlike many machine learning approaches, the rules are easy to read and understand, as evaluated in the paper. Applications include program understanding, modelling-by-example, computational biology, process mining, and cyber-physical systems. The paper appears in a special issue of ASEJ of selected papers from the ASE15 conference. The tool was presented separately in a paper at ASE16. The algorithm was used for Automatic Inference of Rule-Based Specifications of Complex In-place Model Transformations at ICMT17, built upon by work on Recommending Model Refactoring Rules from Refactoring Examples at MODELS18.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -