{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T13:57:54Z","timestamp":1775743074719,"version":"3.50.1"},"reference-count":0,"publisher":"World Scientific Pub Co Pte Ltd","issue":"08","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:p> An integer overflow error occurs when an integer operation in computer software evaluates a value out of the integer range. It can lead to a fatal system failure. The existing approaches to detecting integer overflow errors rely on data\/control-flow analysis of the code or execution of the code with test cases. This paper presents a supervised learning approach to predicting whether each method in a given Java program has an integer overflow error by treating the source code as text. Built upon real-world programs, our Java dataset covers all integer data types and operations in Java, the methods for preventing integer overflow errors, and adversarial samples. We have evaluated six classification models, BERT, DistilBERT, codeBERT, Code2Vec, fastText, and NBSVM. They represent different text embedding techniques for dealing with source code. The experiment results show that BERT and its variants have outperformed other models. We have applied the resultant BERT model to eleven real-world projects, including JDK13.0 and ten top-ranked GitHub projects, and revealed 181 integer overflow errors. In addition, we have evaluated the classification models with a C\/C++ dataset. The result is similar to that of the Java dataset. <\/jats:p>","DOI":"10.1142\/s0218213022500476","type":"journal-article","created":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T11:04:30Z","timestamp":1664103870000},"source":"Crossref","is-referenced-by-count":3,"title":["Predicting Integer Overflow Errors via Supervised Learning"],"prefix":"10.1142","volume":"31","author":[{"given":"Yu","family":"Luo","sequence":"first","affiliation":[{"name":"Computer Science Electrical Engineering, University of Missouri-Kansas City, Kansas City, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weifeng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Criminal Justice, The University of Baltimore, Baltimore, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dianxiang","family":"Xu","sequence":"additional","affiliation":[{"name":"Computer Science Electrical Engineering, University of Missouri-Kansas City, Kansas City, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2022,12,28]]},"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213022500476","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T11:50:45Z","timestamp":1672314645000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0218213022500476"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12]]},"references-count":0,"journal-issue":{"issue":"08","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["10.1142\/S0218213022500476"],"URL":"https:\/\/doi.org\/10.1142\/s0218213022500476","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12]]},"article-number":"2250047"}}