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Grant: Idea to Innovation

University of Waterloo

$125,000.00

Deep defect and vulnerability prediction

FY 2017-18

Summary
The objective of the proposed project is to build a product based on the patented technology from theapplicant's team on detecting software defects and security vulnerabilities automatically. The proposed productwill help developers deliver high-quality software with speed and agility.The technology, referred to as deep defect and vulnerability prediction, predicts source code regions thatcontain defects and security vulnerabilities, i.e., buggy code regions. A core technique is to leverage deeplearning to automatically learn semantic features for building more accurate prediction models to identifydefects and security vulnerabilities. Similar to a weather forecast system that leverages statistical models topredict a rainy day from abundant weather histories, this technology leverages deep learning to automaticallylearn new defect and vulnerability patterns from software development histories. The learned patterns enablethis technology to detect new classes of defects and vulnerabilities.In addition to research contributions, the potential cost savings for the software industry from the proposedproject will be immense because it will significantly reduce the cost of software development and maintenanceby (1) detecting more defects and vulnerabilities earlier (the earlier they are detected, the less they cost), and (2)improving software development productivity
Program
Idea to Innovation
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
505833-2017
Name nom
Tan, Lin
Department département
Electrical & Computer Engineering
Organization id
33
Institution établissement
University of Waterloo
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2017
Competition year année de concours
2017
Award amount
125000
Program id
I2IPJ
Program name en
Idea to Innovation
Program name fr
De l'idée à l'innovation
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
801
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Computer software
Area of application fr
Logiciel
Research subject code
2706
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Software engineering
Research subject fr
Génie logiciel
Partie
1
Nb partie
1
Application title
Deep defect and vulnerability prediction
Keyword
NSERC
Application summary
The objective of the proposed project is to build a product based on the patented technology from theapplicant's team on detecting software defects and security vulnerabilities automatically. The proposed productwill help developers deliver high-quality software with speed and agility.The technology, referred to as deep defect and vulnerability prediction, predicts source code regions thatcontain defects and security vulnerabilities, i.e., buggy code regions. A core technique is to leverage deeplearning to automatically learn semantic features for building more accurate prediction models to identifydefects and security vulnerabilities. Similar to a weather forecast system that leverages statistical models topredict a rainy day from abundant weather histories, this technology leverages deep learning to automaticallylearn new defect and vulnerability patterns from software development histories. The learned patterns enablethis technology to detect new classes of defects and vulnerabilities.In addition to research contributions, the potential cost savings for the software industry from the proposedproject will be immense because it will significantly reduce the cost of software development and maintenanceby (1) detecting more defects and vulnerabilities earlier (the earlier they are detected, the less they cost), and (2)improving software development productivity