Reference number : GC-2017-Q3-00627, amendment number : , agreement type : G, recipient legal name : Guo, Yuhong (Carleton University), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Verifying computer programs is expensive and difficult. The available empirical tests are often_x000D_
time-consuming and ad-hoc providing only a fragmented understanding of the program being analyzed. Static_x000D_
code analysis, which performs computer program debugging by examining the source code without explicitly_x000D_
executing the program, is an invaluable asset for evaluating programs. However, most existing static analysis_x000D_
tools have major drawback of producing unmanageable number of false positives, while requiring significant_x000D_
costly manual interventions. This research project addresses this problem by developing machine learning_x000D_
approaches that increase the agent's ability to detect false vulnerabilities and induce more reliable static_x000D_
analyzers. The project will provide a tangible benefit to the development of Software Secured's flagship_x000D_
product Omega and their clients who will save countless man-hours currently spent triaging false positives._x000D_
It also will have a significant impact on improving the quality of complex softwares in general., program purpose french : Verifying computer programs is expensive and difficult. The available empirical tests are often_x000D_
time-consuming and ad-hoc providing only a fragmented understanding of the program being analyzed. Static_x000D_
code analysis, which performs computer program debugging by examining the source code without explicitly_x000D_
executing the program, is an invaluable asset for evaluating programs. However, most existing static analysis_x000D_
tools have major drawback of producing unmanageable number of false positives, while requiring significant_x000D_
costly manual interventions. This research project addresses this problem by developing machine learning_x000D_
approaches that increase the agent's ability to detect false vulnerabilities and induce more reliable static_x000D_
analyzers. The project will provide a tangible benefit to the development of Software Secured's flagship_x000D_
product Omega and their clients who will save countless man-hours currently spent triaging false positives._x000D_
It also will have a significant impact on improving the quality of complex softwares in general., agreement title english : Improving Static Code Analysis Using Machine Learning Methods, agreement title french : Improving Static Code Analysis Using Machine Learning Methods, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-11-08, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada