The$credit$for$crea-ng$these$slides$belongs$to$ Fall$2014$CS$521/621$students.$$Student$names$ have$been$removed$per$ferpa$regula-ons.

Size: px
Start display at page:

Download "The$credit$for$crea-ng$these$slides$belongs$to$ Fall$2014$CS$521/621$students.$$Student$names$ have$been$removed$per$ferpa$regula-ons."

Transcription

1 The$credit$for$crea-ng$these$slides$belongs$to$ Fall$2014$CS$521/621$students.$$Student$names$ have$been$removed$per$ferpa$regula-ons.$

2 SemFix: Program Repair via Semantic Analysis Hoang Duong Thien Nguyen Dawei Qi Abhik Roychoudhury Satish Chandra enter Electrical Dept and name Computer in Slide Master

3 Background Bug fixing is mostly manual, time consuming and expensive activity Current automatic bug-fixing techniques: Specification-based repair formal specification is needed Genetic-programming-based repair correct expression should be present in program Enumeration-based repair all possible expressions should be considered

4 Research Question Can we fix bugs of a program without formal specification? Is there a better way to fix bugs than genetic programming or enumeration? Is program synthesis better than enumeration?

5 Contribution Provide an automatically bug-fixing tool without formal specification Come up with a Constraint: Requirement to make repaired code pass all given tests Higher success-rate and fast bug repair Provide a new efficient and wide scalability technique to add component synthesis

6 Key Idea Get a Ranked Bug report using statistical fault localization Find a repair constraint according to given tests using symbolic execution Compute a repair for the program using program synthesis

7 Key Idea Ranked bug report generated by Tarantula toolkit -- can use other metric a list contains all faulty statements with location faulty statement ranked by suspiciousness score from most suspicious statement to the least one. o Suspiciousness of a line is how often the line is executed in successful and failing executions. Greater the number of failures, greater the score

8 Key Idea To get repair Constraint C (Symbolic Execution) x = fbuggy( ) -> x=f( ) Input-output pair of each testi generate one constraint Ci 1. symbolic T = f(input) 2. ci is the requirement of T to get the expected output (eg: T>10) Repair Constraint C is the conjunction of Ci

9 Key Idea To solve the repair Constraint C(Program Synthesis) Decide components which can appear in the fix select primitive components based on complexity. define location variables for each component. Generate a repair statement by solving repair constraint done by SMT

10 Example bias = down_sep->bias=f(inhibit,up_sep,down_sep) 1: get bug report T = f(inhibit,up_sep,down_sep) C={(C1 : T<=100) ^ (C2: T>110) ^ ^ (C5: T<10) } 2.Get repair constraint for first bug

11 Example Repair constraint : C={(C1 : T<100) ^ (C2: T>110) ^ ^ (C5: T<10) Provide component for function f(inhibit,up_sep,down_sep) --start with level 1 : function f(inhibit,up_sep,down_sep) = constant --if level 1 cannot satisfy C, combine level 1 and level 2 --process continues until a repair is generated : f(inhibit,up_sep,down_sep) =up_sep + 100

12 Repair Algorithm

13 Summary of Evaluation Subject programs used

14 Summary of Evaluation SemFix versus Genprog (Based on Genetic programming) Success repair rate: Semfix > Genprog (SIR) Overall 90 buggy programs for 50 given tests: Semfix repaired 48/90 GenProg repaired 16/90

15 Summary of Evaluation Bug types: SemFix fixed more types of bugs than GenProg

16 Summary of Evaluation Running time: GenProg running time is greater than 3 times of SemFix (SIR)

17 Summary of Evaluation Repair that were not fixed: Multiple line fix Same wrong branch condition if (c){... }... if (c) {... } Updates to multiple variables x =e1;... ; y =e2; Floating point bugs n = (int) (count*ratio +1.1)

18 Conclusion The SemFix tool can automatically fix bugs without formal specification SemFix has higher success rate than GenProg and runs faster than than the latter. SemFix can fix variable types of bugs

19 Discussion 1. If the termination condition of a loop is an expression over our introduced symbolic variable, the symbolic execution may never terminate. What should we do? For example: while(i<x){x=buggy-expression}

20 Discussion 2. If we set the bound too small, what might happen?

21 Discussion 3. Why SemFix run faster than GenProg tool?

22 Discussion 4. Any other bugs that you can think of which SemFix can t Fix?

23 Discussion 5. Can a test case outside the test suite be used to generate a repair?

24 Discussion 6. Is it easier to fix multiple simple repairs than just one complex repair?

25 Discussion 7. SemFix uses program synthesis which uses different components, levels of statements to generate a repair. Why is this faster than enumeration?

SemFix: Program Repair via Semantic Analysis. Ye Wang, PhD student Department of Computer Science Virginia Tech

SemFix: Program Repair via Semantic Analysis. Ye Wang, PhD student Department of Computer Science Virginia Tech SemFix: Program Repair via Semantic Analysis Ye Wang, PhD student Department of Computer Science Virginia Tech Problem Statement Debugging takes much time and effort Even after root cause of a bug is identified,

More information

DynaMoth: Dynamic Code Synthesis for Automatic Program Repair

DynaMoth: Dynamic Code Synthesis for Automatic Program Repair DynaMoth: Dynamic Code Synthesis for Automatic Program Repair AST 2016 Thomas Durieux & Martin Monperrus March 6, 2017 Inria & University of Lille Automatic test-suite based repair DynaMoth is an automatic

More information

Automatic Repair of Buggy If Conditions and Missing Preconditions with SMT

Automatic Repair of Buggy If Conditions and Missing Preconditions with SMT Automatic Repair of Buggy If Conditions and Missing Preconditions with SMT Favio DeMarco Universidad de Buenos Aires Buenos Aires, Argentina Daniel Le Berre University of Artois & CNRS Lens, France Jifeng

More information

ASTOR: A Program Repair Library for Java

ASTOR: A Program Repair Library for Java ASTOR: A Program Repair Library for Java Matias Martinez University of Lugano, Switzerland Martin Monperrus University of Lille & Inria, France ABSTRACT During the last years, the software engineering

More information

Precise Condition Synthesis for Program Repair

Precise Condition Synthesis for Program Repair Precise Condition Synthesis for Program Repair Yingfei Xiong 1, Jie Wang 1, Runfa Yan 2, Jiachen Zhang 1, Shi Han 3, Gang Huang 1, Lu Zhang 1 1 Peking University 2 University of Electronic Science and

More information

REPAIRING PROGRAMS WITH SEMANTIC CODE SEARCH. Yalin Ke Kathryn T. Stolee Claire Le Goues Yuriy Brun

REPAIRING PROGRAMS WITH SEMANTIC CODE SEARCH. Yalin Ke Kathryn T. Stolee Claire Le Goues Yuriy Brun REPAIRING PROGRAMS WITH SEMANTIC CODE SEARCH Yalin Ke Kathryn T. Stolee Claire Le Goues Yuriy Brun Iowa State Iowa State Carnegie Mellon UMass Amherst 1 Input: buggy program, tests Output: fixed program

More information

Automated Program Repair by Using Similar Code Containing Fix Ingredients

Automated Program Repair by Using Similar Code Containing Fix Ingredients Automated Program Repair by Using Similar Code Containing Fix Ingredients Tao Ji, Liqian Chen, Xiaoguang Mao, Xin Yi College of Computer National University of Defense Technology Changsha, China jitao

More information

DirectFix: Looking for Simple Program Repairs

DirectFix: Looking for Simple Program Repairs DirectFix: Looking for Simple Program Repairs Sergey Mechtaev, Jooyong Yi and Abhik Roychoudhury National University of Singapore {mechtaev,jooyong,abhik@comp.nus.edu.sg Abstract Recent advances in program

More information

Precise Program Repair

Precise Program Repair Precise Program Repair Yingfei Xiong Peking University Background of Yingfei Xiong 2000~2004,UESTC, Undergraduate 2004~2006,Peking University, Graduate Adivsor: Hong Mei, Fuqing Yang 2006~2009,The University

More information

EECS 481 Software Engineering Exam #1. You have 1 hour and 20 minutes to work on the exam.

EECS 481 Software Engineering Exam #1. You have 1 hour and 20 minutes to work on the exam. EECS 481 Software Engineering Exam #1 Write your name and UM uniqname on the exam. There are ten (10) pages in this exam (including this one) and seven (7) questions, each with multiple parts. Some questions

More information

Program Synthesis. SWE 795, Spring 2017 Software Engineering Environments

Program Synthesis. SWE 795, Spring 2017 Software Engineering Environments Program Synthesis SWE 795, Spring 2017 Software Engineering Environments Today HW3 is due next week in class! Part 1 (Lecture)(~50 mins) Break! Part 2 (Discussion)(~60 mins) Discussion of readings Part

More information

An automated approach to program repair with semantic code search

An automated approach to program repair with semantic code search Graduate Theses and Dissertations Iowa State University Capstones, Theses and Dissertations 2015 An automated approach to program repair with semantic code search Yalin Ke Iowa State University Follow

More information

Automatic Repair of Real Bugs in Java: A Large-Scale Experiment on the Defects4J Dataset

Automatic Repair of Real Bugs in Java: A Large-Scale Experiment on the Defects4J Dataset Automatic Repair of Real Bugs in Java: A Large-Scale Experiment on the Defects4J Dataset Matias Martinez, Thomas Durieux, Romain Sommerard, Jifeng Xuan, Martin Monperrus 1 Automatic Software Repair Automatic

More information

A Generic Algorithm for Program Repair

A Generic Algorithm for Program Repair A Generic Algorithm for Program Repair Besma Khaireddine University of Tunis Manar, Tunisia khaireddine.besma@gmail.com Aleksandr Zakharchenko NJIT, Newark NJ az68@njit.edu Ali Mili NJIT, Newark NJ mili@njit.edu

More information

Finding and Fixing Bugs in Liquid Haskell. Anish Tondwalkar

Finding and Fixing Bugs in Liquid Haskell. Anish Tondwalkar Finding and Fixing Bugs in Liquid Haskell Anish Tondwalkar Overview Motivation Liquid Haskell Fault Localization Fault Localization Evaluation Predicate Discovery Predicate Discovery Evaluation Conclusion

More information

Agile Software Development. Lecture 7: Software Testing

Agile Software Development. Lecture 7: Software Testing Agile Software Development Lecture 7: Software Testing Mahmoud El-Gayyar elgayyar@ci.suez.edu.eg Slides are a modified version of the slides by Prof. Kenneth M. Anderson Outline Testing Terminology Types

More information

Evaluating and Improving Fault Localization

Evaluating and Improving Fault Localization Evaluating and Improving Fault Localization Spencer Pearson, Jose Campos*, Rene Just, Gordon Fraser*, Rui Abreu, Michael D. Ernst, Deric Pang, Benjamin Keller University of Massachusetts University of

More information

Leveraging Program Equivalence for Adaptive Program Repair: Models and First Results. Westley Weimer, UVA Zachary P. Fry, UVA Stephanie Forrest, UNM

Leveraging Program Equivalence for Adaptive Program Repair: Models and First Results. Westley Weimer, UVA Zachary P. Fry, UVA Stephanie Forrest, UNM Leveraging Program Equivalence for Adaptive Program Repair: Models and First Results Westley Weimer, UVA Zachary P. Fry, UVA Stephanie Forrest, UNM Automated Program Repair Given a program, a notion of

More information

Alleviating Patch Overfitting with Automatic Test Generation: A Study of Feasibility and Effectiveness for the Nopol Repair System

Alleviating Patch Overfitting with Automatic Test Generation: A Study of Feasibility and Effectiveness for the Nopol Repair System Alleviating Patch Overfitting with Automatic Test Generation: A Study of Feasibility and Effectiveness for the Nopol Repair System Zhongxing Yu Matias Martinez Benjamin Danglot Thomas Durieux Martin Monperrus

More information

Semantic Program Repair Using a Reference Implementation

Semantic Program Repair Using a Reference Implementation Sergey Mechtaev National University of Singapore mechtaev@comp.nus.edu.sg Manh-Dung Nguyen National University of Singapore nguyenmanhdung1710@gmail.com Yannic Noller Humboldt University of Berlin yannic.noller@informatik.hu-berlin.de

More information

Upcoming. Repairing Automated Repair. Generalizing 3/19/18

Upcoming. Repairing Automated Repair. Generalizing 3/19/18 Upcoming Homework 3 due March 22 Literature review due today March 20 Project plan assignment posted, due April 10 Repairing Automated Repair Paper presentation instructions: http://people.cs.umass.edu/~brun/class/2018spring/cs21/paperpresentation/paperpresentation.pdf

More information

Software Debugger. Semester Project Presentation. Group 22. MA Mingyu Derek D. ZHOU Huakang D

Software Debugger. Semester Project Presentation. Group 22. MA Mingyu Derek D. ZHOU Huakang D Software Debugger Semester Project Presentation COMP3211 Software Engineering Department of Computing The Hong Kong Polytechnic University Group 22 MA Mingyu Derek 14110562D WEI Yiran 15063347D ZHOU Huakang

More information

A Survey on Software Fault Localization Techniques

A Survey on Software Fault Localization Techniques Proceedings of the International Conference on Applied Mathematics and Theoretical Computer Science - 2013 189 A Survey on Software Fault Localization Techniques T. Prabakaran and Dr.P. Sengottuvelan Abstract---

More information

Spectrum Mutant. Evaluating and Improving Fault Localization

Spectrum Mutant. Evaluating and Improving Fault Localization Evaluating and Improving Fault Localization Spencer Pearson Michael Ernst Debugging is expensive Your program has a bug. What do you do? Reproduce it Locate it Focus of this talk Fix it Fault localization

More information

Towards the quality improvement of cross-platform mobile applications

Towards the quality improvement of cross-platform mobile applications 2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft) Towards the quality improvement of cross-platform mobile applications Matias Martinez, Sylvain Lecomte

More information

A Systematic Study of Automated Program Repair: Fixing 55 out of 105 Bugs for $8 Each

A Systematic Study of Automated Program Repair: Fixing 55 out of 105 Bugs for $8 Each A Systematic Study of Automated Program Repair: Fixing 55 out of 105 Bugs for $8 Each Claire Le Goues (Virginia), Michael Dewey-Vogt (Virginia), Stephanie Forrest (New Mexico), Westley Weimer (Virginia)

More information

HECTOR: Formal System-Level to RTL Equivalence Checking

HECTOR: Formal System-Level to RTL Equivalence Checking ATG SoC HECTOR: Formal System-Level to RTL Equivalence Checking Alfred Koelbl, Sergey Berezin, Reily Jacoby, Jerry Burch, William Nicholls, Carl Pixley Advanced Technology Group Synopsys, Inc. June 2008

More information

arxiv: v1 [cs.se] 25 Mar 2014

arxiv: v1 [cs.se] 25 Mar 2014 Do the Fix Ingredients Already Exist? An Empirical Inquiry into the Redundancy Assumptions of Program Repair Approaches Matias Martinez Westley Weimer Martin Monperrus University of Lille & INRIA, France

More information

arxiv: v1 [cs.se] 1 Mar 2017

arxiv: v1 [cs.se] 1 Mar 2017 Test Case Generation for Program Repair: A Study of Feasibility and Effectiveness Zhongxing Yu, Matias Martinez, Benjamin Danglot, Thomas Durieux and Martin Monperrus arxiv:1703.00198v1 [cs.se] 1 Mar 2017

More information

Automatically Repairing Broken Workflows for Evolving GUI Applications

Automatically Repairing Broken Workflows for Evolving GUI Applications Automatically Repairing Broken Workflows for Evolving GUI Applications Sai Zhang University of Washington Joint work with: Hao Lü, Michael D. Ernst End-user s workflow A workflow = A sequence of UI actions

More information

Is Coincidental Correctness Less Prevalent in Unit Testing? Wes Masri American University of Beirut Electrical and Computer Engineering Department

Is Coincidental Correctness Less Prevalent in Unit Testing? Wes Masri American University of Beirut Electrical and Computer Engineering Department Is Coincidental Correctness Less Prevalent in Unit Testing? Wes Masri American University of Beirut Electrical and Computer Engineering Department Outline Definitions Weak CC vs. Strong CC Causes of Coincidental

More information

Modular and Verified Automatic Program Repairs

Modular and Verified Automatic Program Repairs Modular and Verified Automatic Program Repairs from Francesco Logozzo and Thomas Ball at Microsoft Research, Redmond presenter name(s) removed for FERPA considerations Introduction Your programs will have

More information

Software security, secure programming

Software security, secure programming Software security, secure programming Fuzzing and Dynamic Analysis Master on Cybersecurity Master MoSiG Academic Year 2017-2018 Outline Fuzzing (or how to cheaply produce useful program inputs) A concrete

More information

Program Repair Using Code Repositories

Program Repair Using Code Repositories Program Repair Using Code Repositories Qi Xin Brown University qx5@cs.brown.edu Steven P. Reiss Brown University spr@cs.brown.edu Shriram Krishnamurthi Brown University sk@cs.brown.edu Abstract We present

More information

Trusted Software Repair for System Resiliency. Westley Weimer, Stephanie Forrest, Miryung Kim, Claire Le Goues, Patrick Hurley

Trusted Software Repair for System Resiliency. Westley Weimer, Stephanie Forrest, Miryung Kim, Claire Le Goues, Patrick Hurley Trusted Software Repair for System Resiliency, Stephanie Forrest, Miryung Kim, Claire Le Goues, Patrick Hurley For The Next 17 Minutes Program Repair: Resilient but Untrusted Can we assess post-repair

More information

1 DARWIN: An Approach for Debugging Evolving Programs

1 DARWIN: An Approach for Debugging Evolving Programs 1 DARWIN: An Approach for Debugging Evolving Programs DAWEI QI, ABHIK ROYCHOUDHURY, ZHENKAI LIANG National University of Singapore and KAPIL VASWANI Microsoft Research India Bugs in programs are often

More information

AUTOMATIC software repair aims to automatically

AUTOMATIC software repair aims to automatically JOURNAL OF L A T E X CLASS FILES, VOL. 6, NO. 1, JANUARY 2007 1 Nopol: Automatic Repair of Conditional Statement Bugs in Java Programs Jifeng Xuan, Member, IEEE, Matias Martinez, Favio DeMarco, Maxime

More information

AUTOMATIC software repair aims to automatically fix

AUTOMATIC software repair aims to automatically fix 34 IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 43, NO. 1, JANUARY 2017 Nopol: Automatic Repair of Conditional Statement Bugs in Java Programs Jifeng Xuan, Member, IEEE, Matias Martinez, Favio DeMarco,

More information

Symbolic Execution. Wei Le April

Symbolic Execution. Wei Le April Symbolic Execution Wei Le 2016 April Agenda What is symbolic execution? Applications History Interal Design: The three challenges Path explosion Modeling statements and environments Constraint solving

More information

Course updates. Repairing Automated Repair. Generalizing 3/20/17. Literature review was due today Project plan assignment is posted, due April 11

Course updates. Repairing Automated Repair. Generalizing 3/20/17. Literature review was due today Project plan assignment is posted, due April 11 3/20/17 Course updates Repairing Automated Repair Literature review was due today Project plan assignment is posted, due April 11 Homework 3 due this Thursday, 9 AM EDT Homework 4 in posted, due April

More information

Formula- Based Software Debugging

Formula- Based Software Debugging DOI:10.1145/2856103 Satisfiability modulo theory solvers can help automate the search for the root cause of observable software errors. BY ABHIK ROYCHOUDHURY AND SATISH CHANDRA Formula- Based Software

More information

Using Execution Paths to Evolve Software Patches

Using Execution Paths to Evolve Software Patches Using Execution Paths to Evolve Software Patches ThanhVu Nguyen*, Westley Weimer**, Claires Le Gouges**, Stephanie Forrest* * University of New Mexico ** University of Virginia Tuesday, March 31, 2009

More information

Sciduction: Combining Induction, Deduction and Structure for Verification and Synthesis

Sciduction: Combining Induction, Deduction and Structure for Verification and Synthesis Sciduction: Combining Induction, Deduction and Structure for Verification and Synthesis (abridged version of DAC slides) Sanjit A. Seshia Associate Professor EECS Department UC Berkeley Design Automation

More information

Connecting Program Synthesis and Reachability:

Connecting Program Synthesis and Reachability: Connecting Program Synthesis and Reachability: Automatic Program Repair using Test-Input Generation ThanhVu Nguyen 1,WestleyWeimer 2, Deepak Kapur 3, and Stephanie Forrest 3 1 University of Nebraska, Lincoln

More information

Cause Clue Clauses: Error Localization using Maximum Satisfiability

Cause Clue Clauses: Error Localization using Maximum Satisfiability Cause Clue Clauses: Error Localization using Maximum Satisfiability Manu Jose University of California, Los Angeles mjose@cs.ucla.edu Rupak Majumdar MPI-SWS, Kaiserslautern & University of California,

More information

DARWIN: An Approach for Debugging Evolving Programs 1

DARWIN: An Approach for Debugging Evolving Programs 1 DARWIN: An Approach for Debugging Evolving Programs 1 Abhik Roychoudhury Kapil Vaswani abhik@nus.edu.sg kapilv@microsoft.com December 2008 Technical Report MSR-TR-2008-91 Microsoft Research Microsoft Corporation

More information

Testing & Symbolic Execution

Testing & Symbolic Execution Testing & Symbolic Execution Software Testing The most common way of measuring & ensuring correctness Input 2 Software Testing The most common way of measuring & ensuring correctness Input Observed Behavior

More information

Chapter 11, Testing. Using UML, Patterns, and Java. Object-Oriented Software Engineering

Chapter 11, Testing. Using UML, Patterns, and Java. Object-Oriented Software Engineering Chapter 11, Testing Using UML, Patterns, and Java Object-Oriented Software Engineering Outline Terminology Types of errors Dealing with errors Quality assurance vs Testing Component Testing! Unit testing!

More information

Anti-patterns in Search-Based Program Repair

Anti-patterns in Search-Based Program Repair Anti-patterns in Search-Based Program Repair Shin Hwei Tan, Hiroaki Yoshida Mukul R. Prasad Abhik Roychoudhury National University of Singapore, Singapore Fujitsu Laboratories of America, Inc., Sunnyvale,

More information

Master s Thesis. Effective Software Fault Localization using Dynamic Program Behaviors

Master s Thesis. Effective Software Fault Localization using Dynamic Program Behaviors 석사 학위논문 Master s Thesis 프로그램의 동적 행동을 이용한 효과적인 소프트웨어 결함 위치추정 Effective Software Fault Localization using Dynamic Program Behaviors 문 석 현 (文 晳 鉉 Moon, Seokhyeon) 전산학과 Department of Computer Science KAIST

More information

Distributed Systems. 09. State Machine Replication & Virtual Synchrony. Paul Krzyzanowski. Rutgers University. Fall Paul Krzyzanowski

Distributed Systems. 09. State Machine Replication & Virtual Synchrony. Paul Krzyzanowski. Rutgers University. Fall Paul Krzyzanowski Distributed Systems 09. State Machine Replication & Virtual Synchrony Paul Krzyzanowski Rutgers University Fall 2016 1 State machine replication 2 State machine replication We want high scalability and

More information

Automated Documentation Inference to Explain Failed Tests

Automated Documentation Inference to Explain Failed Tests Automated Documentation Inference to Explain Failed Tests Sai Zhang University of Washington Joint work with: Cheng Zhang, Michael D. Ernst A failed test reveals a potential bug Before bug-fixing, programmers

More information

Automatically Finding Patches Using Genetic Programming

Automatically Finding Patches Using Genetic Programming Automatically Finding Patches Using Genetic Programming Authors: Westley Weimer, ThanhVu Nguyen, Claire Le Goues, Stephanie Forrest Presented by: David DeBonis, Qi Lu, Shuang Yang Department of Computer

More information

Improving Efficiency and Scalability of Formula-based Debugging

Improving Efficiency and Scalability of Formula-based Debugging Improving Efficiency and Scalability of Formula-based Debugging Wei Jin and Alessandro Orso Georgia Institute of Technology {weijin orso}@gatech.edu arxiv:1409.1989v1 [cs.se] 6 Sep 2014 Abstract Formula-based

More information

Connecting Program Synthesis and Reachability: Automatic Program Repair using Test-Input Generation

Connecting Program Synthesis and Reachability: Automatic Program Repair using Test-Input Generation University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln CSE Technical reports Computer Science and Engineering, Department of 10-19-2016 Connecting Program Synthesis and Reachability:

More information

Partitioning Patches into Test-equivalence Classes for Scaling Program Repair

Partitioning Patches into Test-equivalence Classes for Scaling Program Repair Partitioning Patches into Test-equivalence Classes for Scaling Program Repair arxiv:1707.03139v2 [cs.se] 15 Jul 2017 ABSTRACT Sergey Mechtaev National University of Singapore mechtaev@comp.nus.edu.sg Shin

More information

CASE (Computer-Aided Software Engineering)

CASE (Computer-Aided Software Engineering) CASE (Computer-Aided Software Engineering) Slide 5.1 Scope of CASE Can support the entire life-cycle Graphical display tools (many for PCs) Data flow diagrams Entity-relationship diagrams Module-interconnection

More information

Learning to Synthesize. Yingfei Xiong, Bo Wang, Guirong Fu, Linfei Zang Peking University June 2, 2018

Learning to Synthesize. Yingfei Xiong, Bo Wang, Guirong Fu, Linfei Zang Peking University June 2, 2018 Learning to Synthesize Yingfei Xiong, Bo Wang, Guirong Fu, Linfei Zang Peking University June 2, 2018 Outline GI and L2S Components of L2S Application Conclusion Genetic Improvement GI can systematically

More information

Design and Debug: Essen.al Concepts CS 16: Solving Problems with Computers I Lecture #8

Design and Debug: Essen.al Concepts CS 16: Solving Problems with Computers I Lecture #8 Design and Debug: Essen.al Concepts CS 16: Solving Problems with Computers I Lecture #8 Ziad Matni Dept. of Computer Science, UCSB Outline Midterm# 1 Grades Review of key concepts Loop design help Ch.

More information

An Unsystematic Review of Genetic Improvement. David R. White University of Glasgow UCL Crest Open Workshop, Jan 2016

An Unsystematic Review of Genetic Improvement. David R. White University of Glasgow UCL Crest Open Workshop, Jan 2016 An Unsystematic Review of Genetic Improvement David R. White University of Glasgow UCL Crest Open Workshop, Jan 2016 A Systematic Study of GI is currently under preparation. Justyna Petke Mark Harman Bill

More information

THE TOOLS OF THE TRADE

THE TOOLS OF THE TRADE CHAPTER 5 Slide 5.1 THE TOOLS OF THE TRADE Overview Slide 5.2 Stepwise refinement Cost benefit analysis Software metrics CASE Taxonomy of CASE Scope of CASE Software versions Configuration control Build

More information

Review, Summary and Future Studies

Review, Summary and Future Studies Review, Summary and Future Studies Wei Le Iowa State University April 25, 2018 Outline Important Concepts in Program Analysis Program Representation Control Flow Analysis Program Dependence and Slicing

More information

Debugging as a Science, that too, when your Program is Changing

Debugging as a Science, that too, when your Program is Changing Debugging as a Science, that too, when your Program is Changing Abhik Roychoudhury 1 School of Computing National University of Singapore Abstract Program debugging is an extremely time-consuming process,

More information

AN EFFICIENT SOFTWARE DEFECT PREDICTION MODEL USING OPTIMIZED TABU SEARCH BRANCH AND BOUND PROCEDURE

AN EFFICIENT SOFTWARE DEFECT PREDICTION MODEL USING OPTIMIZED TABU SEARCH BRANCH AND BOUND PROCEDURE AN EFFICIENT SOFTWARE DEFECT PREDICTION MODEL USING OPTIMIZED TABU SEARCH BRANCH AND BOUND PROCEDURE Pandiyan G. 1 and P. Krishnakumari 2 1 RVS College of Arts and Science, Coimbatore, India 2 Department

More information

Constraint-Based Error Localization. Mohammed, Bekkouche, Hélène Collavizza, Michel Rueher. University of Nice Sophia-Antipolis.

Constraint-Based Error Localization. Mohammed, Bekkouche, Hélène Collavizza, Michel Rueher. University of Nice Sophia-Antipolis. Constraint-Based Error Localization Mohammed, Bekkouche, Hélène Collavizza, Michel Rueher University of Nice Sophia-Antipolis France Réunion VACSIM 14 Octobre 2014 I3S CNRS raft 1 Problem 2 Motivating

More information

CSE507. Practical Applications of SAT. Computer-Aided Reasoning for Software. Emina Torlak

CSE507. Practical Applications of SAT. Computer-Aided Reasoning for Software. Emina Torlak Computer-Aided Reasoning for Software CSE507 Practical Applications of SAT courses.cs.washington.edu/courses/cse507/18sp/ Emina Torlak emina@cs.washington.edu Today Past 2 lectures The theory and mechanics

More information

DISTRIBUTED COMPUTING

DISTRIBUTED COMPUTING DISTRIBUTED COMPUTING By Sunita Mahajan & Seema Shah Presented By Prof. S.J. Soni, Asst. Professor, CE Dept., SPCE, Visnagar CHAPTER-1 BASIC DISTRIBUTED SYSTEM CONCEPTS What is a distributed system? Tanenbaum

More information

Leveraging Syntax-Related Code for Automated Program Repair

Leveraging Syntax-Related Code for Automated Program Repair Leveraging Syntax-Related Code for Automated Program Repair Qi Xin, Steven P. Reiss Department of Computer Science Brown University Providence, RI, USA {qx5,spr}@cs.brown.edu Abstract We present our automated

More information

CS2104 Prog. Lang. Concepts

CS2104 Prog. Lang. Concepts CS2104 Prog. Lang. Concepts Operational Semantics Abhik Roychoudhury Department of Computer Science National University of Singapore Organization An imperative language IMP Formalizing the syntax of IMP

More information

Identifying Patch Correctness in Test-Based Program Repair

Identifying Patch Correctness in Test-Based Program Repair Key Laboratory of High Confidence Software Technologies (Peking University), MoE Institute of Software, EECS, Peking University, Beijing, 100871, China {xiongyf,liuxinyuan,mhzeng,zhanglucs,hg}@pku.edu.cn

More information

Automatically Finding Patches Using Genetic Programming

Automatically Finding Patches Using Genetic Programming Automatically Finding Patches Using Genetic Programming Westley Weimer, Stephanie Forrest, Claire Le Goues, ThanVu Nguyen, Ethan Fast, Briana Satchell, Eric Schulte Motivation Software Quality remains

More information

Processes and Threads. Processes: Review

Processes and Threads. Processes: Review Processes and Threads Processes and their scheduling Threads and scheduling Multiprocessor scheduling Distributed Scheduling/migration Lecture 3, page 1 Processes: Review Multiprogramming versus multiprocessing

More information

Fault Localization Using Value Replacement

Fault Localization Using Value Replacement Fault Localization Using Value Replacement Dennis Jeffrey (1) jeffreyd@cs.ucr.edu Neelam Gupta guptajneelam@gmail.com Rajiv Gupta (1) gupta@cs.ucr.edu (1) Univ. of California at Riverside, CSE Department,

More information

CSE 374 Programming Concepts & Tools. Hal Perkins Fall 2015 Lecture 15 Testing

CSE 374 Programming Concepts & Tools. Hal Perkins Fall 2015 Lecture 15 Testing CSE 374 Programming Concepts & Tools Hal Perkins Fall 2015 Lecture 15 Testing Where we are Some very basic software engineering topics in the midst of tools Today: testing (how, why, some terms) Later:

More information

The Mercury project. Zoltan Somogyi

The Mercury project. Zoltan Somogyi The Mercury project Zoltan Somogyi The University of Melbourne Linux Users Victoria 7 June 2011 Zoltan Somogyi (Linux Users Victoria) The Mercury project June 15, 2011 1 / 23 Introduction Mercury: objectives

More information

GENETIC PROGRAMMING FOR SOFTWARE TRANSPLANTS IMAN HEMATI MOGHADAM

GENETIC PROGRAMMING FOR SOFTWARE TRANSPLANTS IMAN HEMATI MOGHADAM GENETIC PROGRAMMING FOR SOFTWARE TRANSPLANTS IMAN HEMATI MOGHADAM IMPLEMENTED APPROACH: OVERVIEW 1/20 SLICING: Forward Slicing: Used to extract the implementation of the desired feature. Backward Slicing:

More information

Programming in C++ Prof. Partha Pratim Das Department of Computer Science and Engineering Programming in C++ Indian Institute of Technology, Kharagpur

Programming in C++ Prof. Partha Pratim Das Department of Computer Science and Engineering Programming in C++ Indian Institute of Technology, Kharagpur Programming in C++ Prof. Partha Pratim Das Department of Computer Science and Engineering Programming in C++ Indian Institute of Technology, Kharagpur Lecture 14 Default Parameters and Function Overloading

More information

DARWIN: An Approach for Debugging Evolving Programs

DARWIN: An Approach for Debugging Evolving Programs DARWIN: An Approach for Debugging Evolving Programs Dawei Qi, Abhik Roychoudhury, Zhenkai Liang, Kapil Vaswani National University of Singapore Microsoft Research India {dawei,abhik,liangzk@comp.nus.edu.sg

More information

Verification and Validation

Verification and Validation Steven Zeil February 13, 2013 Contents 1 The Process 3 1 2 Non-Testing V&V 7 2.1 Code Review....... 8 2.2 Mathematically-based verification......................... 19 2.3 Static analysis tools... 23 2.4

More information

Verification and Validation

Verification and Validation Steven Zeil February 13, 2013 Contents 1 The Process 2 2 Non-Testing V&V 3 2.1 Code Review........... 4 2.2 Mathematically-based verification.................................. 8 2.3 Static analysis tools.......

More information

ExCAPE Kick off Meeting. Leads: Stéphane Lafortune and Steve Zdancewic

ExCAPE Kick off Meeting. Leads: Stéphane Lafortune and Steve Zdancewic Education and Outreach ExCAPE Kick off Meeting Monday June 4, 4:30 5:30pm Leads: Stéphane Lafortune and Steve Zdancewic Education and Outreach 2013 Summer School Programs for High SchoolStudents Students

More information

Static & Dynamic Testing using Tools. Created by Kudakwashe K. Chambwe

Static & Dynamic Testing using Tools. Created by Kudakwashe K. Chambwe Testing Tools Static & Dynamic Testing using Tools Created by Kudakwashe K. Chambwe Motivation We will not mention any commercial tools in this chapter, [ ] this book would date very quickly. Tool support

More information

Testing, Fuzzing, & Symbolic Execution

Testing, Fuzzing, & Symbolic Execution Testing, Fuzzing, & Symbolic Execution Software Testing The most common way of measuring & ensuring correctness Input 2 Software Testing The most common way of measuring & ensuring correctness Input Observed

More information

Static and dynamic Testing

Static and dynamic Testing Static and dynamic Testing Static testing Requirements specification High-level design Formal specification Detailed design Program Prototype Dynamic testing Ian Sommerville 1995 Software Engineering,

More information

IntroClassJava: A Benchmark of 297 Small and Buggy Java Programs

IntroClassJava: A Benchmark of 297 Small and Buggy Java Programs IntroClassJava: A Benchmark of 297 Small and Buggy Java Programs Thomas Durieux, Martin Monperrus To cite this version: Thomas Durieux, Martin Monperrus. IntroClassJava: A Benchmark of 297 Small and Buggy

More information

Program Synthesis for Data Manipulation. Gustavo Soares Federal University of Campina Grande

Program Synthesis for Data Manipulation. Gustavo Soares Federal University of Campina Grande Program Synthesis for Data Manipulation Gustavo Soares Federal University of Campina Grande gsoares@dsc.ufcg.edu.br 1 2 3 4 ... Customer Pull Up Method Customer... createbill() Regular Customer... createbill()

More information

Towards Practical Program Repair with On-Demand Candidate Generation

Towards Practical Program Repair with On-Demand Candidate Generation Towards Practical Program Repair with On-Demand Candidate Generation The University of Texas at Austin, USA {lisahua,mengshi.zhang,kaiyuanw,khurshid}@utexas.edu ABSTRACT Effective program repair techniques,

More information

Automated Whitebox Fuzz Testing. by - Patrice Godefroid, - Michael Y. Levin and - David Molnar

Automated Whitebox Fuzz Testing. by - Patrice Godefroid, - Michael Y. Levin and - David Molnar Automated Whitebox Fuzz Testing by - Patrice Godefroid, - Michael Y. Levin and - David Molnar OUTLINE Introduction Methods Experiments Results Conclusion Introduction Fuzz testing is an effective Software

More information

Database-Aware Fault Localization for Dynamic Web Applications

Database-Aware Fault Localization for Dynamic Web Applications Database-Aware Fault Localization for Dynamic Web Applications Hung Viet Nguyen, Hoan Anh Nguyen, Tung Thanh Nguyen, Tien N. Nguyen Iowa State University ICSM 2013 Sep 22-28, 2013 Eindhoven, The Netherlands

More information

No model may be available. Software Abstractions. Recap on Model Checking. Model Checking for SW Verif. More on the big picture. Abst -> MC -> Refine

No model may be available. Software Abstractions. Recap on Model Checking. Model Checking for SW Verif. More on the big picture. Abst -> MC -> Refine No model may be available Programmer Software Abstractions Tests Coverage Code Abhik Roychoudhury CS 5219 National University of Singapore Testing Debug Today s lecture Abstract model (Boolean pgm.) Desirable

More information

Internet Services and Search Engines. Amin Vahdat CSE 123b May 2, 2006

Internet Services and Search Engines. Amin Vahdat CSE 123b May 2, 2006 Internet Services and Search Engines Amin Vahdat CSE 123b May 2, 2006 Midterm: May 9 Annoucements Second assignment due May 15 Lessons from Giant-Scale Services Service Replication Service Partitioning

More information

Additional Slides to De Micheli Book

Additional Slides to De Micheli Book Additional Slides to De Micheli Book Sungho Kang Yonsei University Design Style - Decomposition 08 3$9 0 Behavioral Synthesis Resource allocation; Pipelining; Control flow parallelization; Communicating

More information

Changes in JHawk 6 1 Changes in JHawk6 Virtual Machinery 2015

Changes in JHawk 6 1 Changes in JHawk6 Virtual Machinery 2015 Changes in JHawk 6 1 Changes in JHawk6 Virtual Machinery 2015 Contents Changes in JHawk6... 3 Line counting... 4 System Level... 4 Compilation Unit Level... 4 Package Level... 4 Class Level... 4 Method

More information

Ultra Depedable VLSI by Collaboration of Formal Verifications and Architectural Technologies

Ultra Depedable VLSI by Collaboration of Formal Verifications and Architectural Technologies Ultra Depedable VLSI by Collaboration of Formal Verifications and Architectural Technologies CREST-DVLSI - Fundamental Technologies for Dependable VLSI Systems - Masahiro Fujita Shuichi Sakai Masahiro

More information

CMSC 330: Organization of Programming Languages. OCaml Imperative Programming

CMSC 330: Organization of Programming Languages. OCaml Imperative Programming CMSC 330: Organization of Programming Languages OCaml Imperative Programming CMSC330 Fall 2017 1 So Far, Only Functional Programming We haven t given you any way so far to change something in memory All

More information

Automatically Locating software Errors using Interesting Value Mapping Pair (IVMP)

Automatically Locating software Errors using Interesting Value Mapping Pair (IVMP) 71 Automatically Locating software Errors using Interesting Value Mapping Pair (IVMP) Ajai Kumar 1, Anil Kumar 2, Deepti Tak 3, Sonam Pal 4, 1,2 Sr. Lecturer, Krishna Institute of Management & Technology,

More information

Feature Selection. CE-725: Statistical Pattern Recognition Sharif University of Technology Spring Soleymani

Feature Selection. CE-725: Statistical Pattern Recognition Sharif University of Technology Spring Soleymani Feature Selection CE-725: Statistical Pattern Recognition Sharif University of Technology Spring 2013 Soleymani Outline Dimensionality reduction Feature selection vs. feature extraction Filter univariate

More information

Second assignment came out Monday evening. Find defects in Hnefetafl rules written by your classmates. Topic: Code Inspection and Testing

Second assignment came out Monday evening. Find defects in Hnefetafl rules written by your classmates. Topic: Code Inspection and Testing Announcements Second assignment came out Monday evening Topic: Code Inspection and Testing Find defects in Hnefetafl rules written by your classmates Compare inspection, coverage testing, random testing,

More information

Writing a letter quiz

Writing a letter quiz Writing a letter quiz Level A 1. Which type of letter is not likely to be formal? A) making a booking B) making a complaint C) email to a friend D) confirming an interview date 2. True or false? When writing

More information

Feature selection. LING 572 Fei Xia

Feature selection. LING 572 Fei Xia Feature selection LING 572 Fei Xia 1 Creating attribute-value table x 1 x 2 f 1 f 2 f K y Choose features: Define feature templates Instantiate the feature templates Dimensionality reduction: feature selection

More information