Ten years of hunting for similar code for fun and profit (keynote)

• Main Authors: GLONDU, Stephane, JIANG, Lingxiao, SU, Zhendong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: code vectorization; code similarity; code search; code learning; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4283; https://ink.library.smu.edu.sg/context/sis_research/article/5286/viewcontent/Ten_Years_of_Hunting.pdf
• Institution: Singapore Management University

In 2007, the Deckard paper was published at ICSE. Since its publication, it has led to much follow-up research and applications. The paper made two core contributions: a novel vector embedding of structured code for fast similarity detection, and an application of the embedding for clone detection, resulting in the Deckard tool. The vector embedding is simple and easy to adapt. Similar code detection is also fundamental for a range of classical and emerging problems in software engineering, security, and computer science education (e.g., code reuse, refactoring, porting, translation, synthesis, program repair, malware detection, and feedback generation). Both have buttressed the paper’s influence.In 2018, the Deckard paper received the ACM SIGSOFT Impact Paper award. In this keynote, we take the opportunity to review the work’s inception, evolution and impact on its subsequent work and applications, and to share our thoughts on exciting ongoing and future developments.