PPT-Automatic Inference of Code Transforms and Search Spaces for Patch Generation

Author : alexa-scheidler | Published Date : 2018-03-14

Fan Long MIT EECS amp CSAIL 1 Negative Inputs Positive Inputs Generate and Validate Patching Validate each candidate patch against the test suite p gt f1

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Automatic Inference of Code Transforms and Search Spaces for Patch Generation: Transcript

Fan Long MIT EECS amp CSAIL 1 Negative Inputs Positive Inputs Generate and Validate Patching Validate each candidate patch against the test suite p gt f1 . Rahul Sharma and Alex Aiken (Stanford University). 1. Randomized Search. x. = . i. ;. y = j;. while . y!=0 . do. . x = x-1;. . y = y-1;. if( . i. ==j ). assert x==0. No!. Yes!. . 2. Invariants. Techniques and Implications. David . Brumley. , . Pongsin. . Poosankam. , Dawn Song and Jiang . Zheng. Presented by Nimrod . Partush. Outline. Introduction & Motivation. Problem Definition. Problem Solution. S. ignature Generation. . of . Vulnerability-based . Signature. By David . Brumley. , James Newsome, Dawn Song and . Hao. Wang and . Somesh. . Jha. Part I. Presenter: . Xin. Zhao. Definition. Vulnerability - . Zichao Qi. , Fan Long, Sara . Achour. , and. Martin . Rinard. MIT CSAIL. Buggy Program. Generate-And-Validate . Patch . G. eneration . Systems. Test suite of test cases. Candidate patch space. Generate-And-Validate . Simplifiers based . on Conditional Rewrite Rules. Research Qualifying Examination. 9-Oct-15. Automatic Generation of Formula Simplifiers. 1. Rohit Singh. . . Adviser. : Armando Solar-. Fan Long. and Martin . Rinard. MIT EECS & CSAIL. 1. Application. =. Negative. Inputs. =. Positive. Inputs. ≠. ≠. Test Suite. Inputs. Correct. Outputs. Context. =. =. =. Goal. Automatic Patch Generation System. Presenter: . Kaushik Chowdhury. Next. . . GE. neration. . NE. tworks. and. . SYS. tems. . Lab . Authors: . Benjamin Drozdenko. Ramanathan Subramanian. Kaushik Chowdhury. Miriam Leeser. 2. AIM OF THE PROJECT. . 0368-3500. Nurit. . Dor. Shir. Landau-. Feibish. Noam Rinetzky. Preliminaries. Students will group in teams of 2-3 students. . Each group will do one of the projects presented.. Administration. . of . Vulnerability-based . Signature. By David . Brumley. , James Newsome, Dawn Song and . Hao. Wang and . Somesh. . Jha. Part I. Presenter: . Xin. Zhao. Definition. Vulnerability - . A vulnerability is a type of bug that can be used by an attacker to alter the intended operation of the software in a malicious way.. Cyberworld. May. 2017. Sandrine Ammann. Marketing & . C. ommunications Officer. Agenda. Latest development. CLIR. What is CLIR?. How to use it?. Why is it useful?. How . was it developed. ?. Q & A session. Rahul Sharma and Alex Aiken (Stanford University). 1. Randomized Search. x. = . i. ;. y = j;. while . y!=0 . do. . x = x-1;. . y = y-1;. if( . i. ==j ). assert x==0. No!. Yes!. . 2. Invariants. . of . Vulnerability-based . Signature. By David . Brumley. , James Newsome, Dawn Song and . Hao. Wang and . Somesh. . Jha. Part I. Presenter: . Xin. Zhao. Definition. Vulnerability - . A vulnerability is a type of bug that can be used by an attacker to alter the intended operation of the software in a malicious way.. Introduction to Text Generation 杨润琦 Overview Text generation basics Definition and basic architectures Evaluation metrics Major problems and progress Better network architecture From greedy search to beam search E. X. . Why. , . How. , . and. . What. Martin Ruckert, August 2021. Fakultät für Informatik und. Mathematk. Why. ? . Why do we want . cweb Source Code of . T. E. X. ?. Toolchain . Modification. Debugging.

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