{"title":"An experiment in scientific program understanding","authors":"M. Stewart","doi":"10.1109/ASE.2000.873678","DOIUrl":null,"url":null,"abstract":"This paper concerns automated analysis of the meaning or semantics of scientific and engineering code. The procedure involves taking a user's existing code, adding semantic declarations for some primitive variables, and automatically identifying formulae. Parsers encode domain knowledge and recognize formulae in different disciplines including physics, numerical methods, mathematics, and geometry. The parsers will automatically recognize and document some static, semantic concepts and help locate some program semantic errors. Results are shown for three intensively studied codes and seven blind test cases; all test cases are state of the art scientific codes. These techniques may apply to a wider range of scientific codes. If so, the techniques could reduce the time, risk, and effort required to develop and modify scientific codes.","PeriodicalId":206612,"journal":{"name":"Proceedings ASE 2000. Fifteenth IEEE International Conference on Automated Software Engineering","volume":"293 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings ASE 2000. Fifteenth IEEE International Conference on Automated Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASE.2000.873678","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
This paper concerns automated analysis of the meaning or semantics of scientific and engineering code. The procedure involves taking a user's existing code, adding semantic declarations for some primitive variables, and automatically identifying formulae. Parsers encode domain knowledge and recognize formulae in different disciplines including physics, numerical methods, mathematics, and geometry. The parsers will automatically recognize and document some static, semantic concepts and help locate some program semantic errors. Results are shown for three intensively studied codes and seven blind test cases; all test cases are state of the art scientific codes. These techniques may apply to a wider range of scientific codes. If so, the techniques could reduce the time, risk, and effort required to develop and modify scientific codes.