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Using active learning to synthesize models of applications that access databases

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Martin C. RinardAuthors Info & Claims

PLDI 2019: Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation

Pages 269 - 285

https://doi.org/10.1145/3314221.3314591

Published: 08 June 2019 Publication History

Abstract

We present Konure, a new system that uses active learning to infer models of applications that access relational databases. Konure comprises a domain-specific language (each model is a program in this language) and associated inference algorithm that infers models of applications whose behavior can be expressed in this language. The inference algorithm generates inputs and database configurations, runs the application, then observes the resulting database traffic and outputs to progressively refine its current model hypothesis. Because the technique works with only externally observable inputs, outputs, and database configurations, it can infer the behavior of applications written in arbitrary languages using arbitrary coding styles (as long as the behavior of the application is expressible in the domain-specific language). Konure also implements a regenerator that produces a translated Python implementation of the application that systematically includes relevant security and error checks.

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References

[1]

2018. Enki. https://github.com/xaviershay/enki .

[2]

2018. Fulcrum. https://github.com/fulcrum-agile/fulcrum .

[3]

2018. Getting Started with Rails. http://guides.rubyonrails.org/getting_ started.html .

[4]

2018. Kandan – Modern Open Source Chat. https://github.com/ kandanapp/kandan .

[5]

2019. PLDI 2019 Konure Code. http://people.csail.mit.edu/jiasi/ pldi2019.code/ and https://people.csail.mit.edu/rinard/paper/pldi2019. code/ .

[6]

F. Aarts, J. De Ruiter, and E. Poll. 2013. Formal Models of Bank Cards for Free. In 2013 IEEE Sixth International Conference on Software Testing, Verification and Validation Workshops. 461–468.

Digital Library

[7]

Fides Aarts and Frits Vaandrager. 2010. Learning I/O Automata. Springer Berlin Heidelberg, Berlin, Heidelberg, 71–85.

Digital Library

[8]

M. H. Alalfi, J. R. Cordy, and T. R. Dean. 2009. WAFA: Fine-grained dynamic analysis of web applications. In 2009 11th IEEE International Symposium on Web Systems Evolution. 141–150.

[9]

Rajeev Alur, Rastislav Bodík, Garvit Juniwal, Milo M. K. Martin, Mukund Raghothaman, Sanjit A. Seshia, Rishabh Singh, Armando Solar-Lezama, Emina Torlak, and Abhishek Udupa. 2013. Syntaxguided synthesis. In Formal Methods in Computer-Aided Design, FM-CAD 2013, Portland, OR, USA, October 20-23, 2013. 1–8.

[10]

Dana Angluin. 1987. Learning Regular Sets from Queries and Counterexamples. Inf. Comput. 75, 2 (Nov. 1987), 87–106.

Digital Library

[11]

Dana Angluin and Carl H. Smith. 1983. Inductive Inference: Theory and Methods. ACM Comput. Surv. 15, 3 (Sept. 1983), 237–269.

Digital Library

[12]

Sruthi Bandhakavi, Prithvi Bisht, P. Madhusudan, and V. N. Venkatakrishnan. 2007. CANDID: Preventing Sql Injection Attacks Using Dynamic Candidate Evaluations (CCS ’07). 13.

Digital Library

[13]

Gilles Barthe, Juan Manuel Crespo, Sumit Gulwani, Cesar Kunz, and Mark Marron. 2013. From Relational Verification to SIMD Loop Synthesis. In Proceedings of the 18th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP ’13). ACM, New York, NY, USA, 123–134.

Digital Library

[14]

Osbert Bastani, Rahul Sharma, Alex Aiken, and Percy Liang. 2017. Synthesizing program input grammars. In Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2017, Barcelona, Spain, June 18-23, 2017. 95–110.

Digital Library

[15]

Osbert Bastani, Rahul Sharma, Alex Aiken, and Percy Liang. 2018. Active Learning of Points-to Specifications. In Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2018). ACM, New York, NY, USA, 678–692.

Digital Library

[16]

Tewodros A. Beyene, Swarat Chaudhuri, Corneliu Popeea, and Andrey Rybalchenko. 2015. Recursive Games for Compositional Program Synthesis. In Verified Software: Theories, Tools, and Experiments - 7th International Conference, VSTTE 2015, San Francisco, CA, USA, July 18-19, 2015. Revised Selected Papers. 19–39.

[17]

Prithvi Bisht, P. Madhusudan, and V. N. Venkatakrishnan. 2010. CAN-DID: Dynamic Candidate Evaluations for Automatic Prevention of SQL Injection Attacks. ACM Trans. Inf. Syst. Secur. 13, 2, Article 14 (March 2010), 39 pages.

Digital Library

[18]

Aaron R Bradley and Zohar Manna. 2007. The calculus of computation: decision procedures with applications to verification. Springer Science & Business Media.

Digital Library

[19]

Cristian Cadar, Vijay Ganesh, Peter M. Pawlowski, David L. Dill, and Dawson R. Engler. 2006. EXE: Automatically Generating Inputs of Death. In Proceedings of the 13th ACM Conference on Computer and Communications Security (CCS ’06). ACM, New York, NY, USA, 322– 335.

Digital Library

[20]

Sofia Cassel, Falk Howar, Bengt Jonsson, and Bernhard Steffen. 2016. Active learning for extended finite state machines. Formal Aspects of Computing 28, 2 (2016), 233–263.

Digital Library

[21]

Alvin Cheung, Armando Solar-Lezama, and Samuel Madden. 2013. Optimizing Database-backed Applications with Query Synthesis. In Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI ’13). ACM, New York, NY, USA, 3–14.

Digital Library

[22]

T. S. Chow. 1978. Testing Software Design Modeled by Finite-State Machines. IEEE Trans. Softw. Eng. 4, 3 (May 1978), 178–187.

Digital Library

[23]

Shumo Chu, Chenglong Wang, Konstantin Weitz, and Alvin Cheung. 2017. Cosette: An Automated Prover for SQL. In CIDR 2017, 8th Biennial Conference on Innovative Data Systems Research, Chaminade, CA, USA, January 8-11, 2017, Online Proceedings. http://cidrdb.org/cidr2017/ papers/p51-chu-cidr17.pdf

[24]

Anthony Cleve, Nesrine Noughi, and Jean-Luc Hainaut. 2013. Dynamic program analysis for database reverse engineering. In Generative and Transformational Techniques in Software Engineering IV. Springer, 297– 321.

[25]

Yossi Cohen and Yishai A. Feldman. 2003. Automatic High-quality Reengineering of Database Programs by Abstraction, Transformation and Reimplementation. ACM Trans. Softw. Eng. Methodol. 12, 3 (July 2003), 285–316.

Digital Library

[26]

Bas Cornelissen, Andy Zaidman, Arie van Deursen, Leon Moonen, and Rainer Koschke. 2009. A Systematic Survey of Program Comprehension Through Dynamic Analysis. IEEE Trans. Softw. Eng. 35, 5 (Sept. 2009), 684–702.

Digital Library

[27]

Kathi Hogshead Davis and Peter H. Aiken. 2000. Data Reverse Engineering: A Historical Survey. In Proceedings of the Seventh Working Conference on Reverse Engineering (WCRE’00) (WCRE ’00). IEEE Computer Society, Washington, DC, USA, 70–.

Digital Library

[28]

Joeri De Ruiter and Erik Poll. 2015. Protocol State Fuzzing of TLS Implementations. In Proceedings of the 24th USENIX Conference on Security Symposium (SEC’15). USENIX Association, Berkeley, CA, USA, 193–206.

Digital Library

[29]

Kevin Ellis, Armando Solar-Lezama, and Josh Tenenbaum. 2016. Sampling for Bayesian Program Learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain. 1289– 1297.

Digital Library

[30]

Yu Feng, Ruben Martins, Osbert Bastani, and Isil Dillig. 2018. Program synthesis using conflict-driven learning. In Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2018, Philadelphia, PA, USA, June 18-22, 2018. 420–435.

Digital Library

[31]

Yu Feng, Ruben Martins, Jacob Van Geffen, Isil Dillig, and Swarat Chaudhuri. 2017. Component-based synthesis of table consolidation and transformation tasks from examples. In Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2017, Barcelona, Spain, June 18-23, 2017. 422–436.

Digital Library

[32]

John K. Feser, Swarat Chaudhuri, and Isil Dillig. 2015. Synthesizing data structure transformations from input-output examples. In Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation, Portland, OR, USA, June 15-17, 2015. 229–239.

Digital Library

[33]

Paul Fiterău-Broştean, Ramon Janssen, and Frits Vaandrager. 2016. Combining Model Learning and Model Checking to Analyze TCP Implementations. Springer International Publishing, Cham, 454–471.

[34]

X. Fu, X. Lu, B. Peltsverger, S. Chen, K. Qian, and L. Tao. 2007. A Static Analysis Framework For Detecting SQL Injection Vulnerabilities (COMPSAC 2007).

Digital Library

[35]

Vijay Ganesh, Tim Leek, and Martin Rinard. 2009. Taint-based Directed Whitebox Fuzzing. In Proceedings of the 31st International Conference on Software Engineering (ICSE ’09). IEEE Computer Society, Washington, DC, USA, 474–484.

Digital Library

[36]

Timon Gehr, Dimitar Dimitrov, and Martin T. Vechev. 2015. Learning Commutativity Specifications. In Computer Aided Verification - 27th International Conference, CAV 2015, San Francisco, CA, USA, July 18-24, 2015, Proceedings, Part I. 307–323.

[37]

Patrice Godefroid, Nils Klarlund, and Koushik Sen. 2005. DART: Directed Automated Random Testing. In Proceedings of the 2005 ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI ’05). ACM, New York, NY, USA, 213–223.

Digital Library

[38]

Patrice Godefroid, Michael Y. Levin, and David Molnar. 2012. SAGE: Whitebox Fuzzing for Security Testing. Queue 10, 1, Article 20 (Jan. 2012), 8 pages.

Digital Library

[39]

Olga Grinchtein, Bengt Jonsson, and Martin Leucker. 2010. Learning of Event-recording Automata. Theor. Comput. Sci. 411, 47 (Oct. 2010), 4029–4054.

Digital Library

[40]

Sumit Gulwani, Oleksandr Polozov, and Rishabh Singh. 2017. Program Synthesis. Foundations and Trends in Programming Languages 4, 1-2 (2017), 1–119.

[41]

B. P. Gupta, D. Vira, and S. Sudarshan. 2010. X-data: Generating test data for killing SQL mutants. In 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010). 876–879.

[42]

Raju Halder and Agostino Cortesi. 2010. Obfuscation-based Analysis of SQL Injection Attacks (ISCC ’10). 931–938.

Digital Library

[43]

William G. J. Halfond and Alessandro Orso. 2005. AMNESIA: Analysis and Monitoring for NEutralizing SQL-injection Attacks (ASE ’05). 174– 183.

Digital Library

[44]

Stefan Heule, Manu Sridharan, and Satish Chandra. 2015. Mimic: computing models for opaque code. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2015, Bergamo, Italy, August 30 - September 4, 2015. 710–720.

Digital Library

[45]

Malte Isberner, Falk Howar, and Bernhard Steffen. 2014. The TTT Algorithm: A Redundancy-Free Approach to Active Automata Learning. Springer International Publishing, Cham, 307–322.

[46]

Jinseong Jeon, Xiaokang Qiu, Jonathan Fetter-Degges, Jeffrey S. Foster, and Armando Solar-Lezama. 2016. Synthesizing framework models for symbolic execution. In Proceedings of the 38th International Conference on Software Engineering, ICSE 2016, Austin, TX, USA, May 14-22, 2016. 156–167.

Digital Library

[47]

Jinseong Jeon, Xiaokang Qiu, Jeffrey S. Foster, and Armando SolarLezama. 2015. JSketch: sketching for Java. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2015, Bergamo, Italy, August 30 - September 4, 2015. 934–937.

Digital Library

[48]

Susmit Jha, Sumit Gulwani, Sanjit A. Seshia, and Ashish Tiwari. 2010. Oracle-guided Component-based Program Synthesis. In Proceedings of the 32Nd ACM/IEEE International Conference on Software Engineering - Volume 1 (ICSE ’10). ACM, New York, NY, USA, 215–224.

Digital Library

[49]

Nenad Jovanovic, Christopher Kruegel, and Engin Kirda. 2006. Pixy: A Static Analysis Tool for Detecting Web Application Vulnerabilities (Short Paper) (SP ’06). 6.

Digital Library

[50]

V. Benjamin Livshits and Monica S. Lam. 2005. Finding Security Vulnerabilities in Java Applications with Static Analysis (SSYM’05). 18–18.

Digital Library

[51]

Fan Long, Vijay Ganesh, Michael Carbin, Stelios Sidiroglou, and Martin Rinard. 2012. Automatic Input Rectification. In Proceedings of the 34th International Conference on Software Engineering (ICSE ’12). IEEE Press, Piscataway, NJ, USA, 80–90. http://dl.acm.org/citation.cfm?id= 2337223.2337233

[52]

Edward F Moore. 1956. Gedanken-experiments on sequential machines. Automata studies 34 (1956), 129–153.

[53]

Nesrine Noughi, Marco Mori, Loup Meurice, and Anthony Cleve. 2014. Understanding the Database Manipulation Behavior of Programs. In Proceedings of the 22Nd International Conference on Program Comprehension (ICPC 2014). ACM, New York, NY, USA, 64–67.

Digital Library

[54]

Daniel Perelman, Sumit Gulwani, Dan Grossman, and Peter Provost. 2014. Test-driven Synthesis. In Proceedings of the 35th ACM SIG-PLAN Conference on Programming Language Design and Implementation (PLDI ’14). ACM, New York, NY, USA, 408–418.

Digital Library

[55]

Jeff Perkins, Jordan Eikenberry, Alessandro Coglio, Daniel Willenson, Stelios Sidiroglou-Douskos, and Martin Rinard. 2016. AutoRand: Automatic Keyword Randomization to Prevent Injection Attacks. In Proceedings of the 13th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment - Volume 9721 (DIMVA 2016). Springer-Verlag New York, Inc., New York, NY, USA, 37–57.

Digital Library

[56]

Nadia Polikarpova, Ivan Kuraj, and Armando Solar-Lezama. 2016. Program synthesis from polymorphic refinement types. In Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2016, Santa Barbara, CA, USA, June 13-17, 2016. 522–538.

Digital Library

[57]

Yewen Pu, Zachery Miranda, Armando Solar-Lezama, and Leslie Kaelbling. 2018. Selecting Representative Examples for Program Synthesis. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research), Vol. 80. PMLR, 4161–4170. http://proceedings.mlr.press/v80/pu18b.html

[58]

Arjun Radhakrishna, Nicholas V. Lewchenko, Shawn Meier, Sergio Mover, Krishna Chaitanya Sripada, Damien Zufferey, Bor-Yuh Evan Chang, and Pavol Černý. 2018. DroidStar: Callback Typestates for Android Classes. In Proceedings of the 40th International Conference on Software Engineering (ICSE ’18). ACM, New York, NY, USA, 1160–1170.

Digital Library

[59]

Harald Raffelt, Bernhard Steffen, and Therese Berg. 2005. LearnLib: A Library for Automata Learning and Experimentation. In Proceedings of the 10th International Workshop on Formal Methods for Industrial Critical Systems (FMICS ’05). ACM, New York, NY, USA, 62–71.

Digital Library

[60]

George Reese. 2000. Database Programming with JDBC and JAVA. O’Reilly Media, Inc.

Digital Library

[61]

Martin C. Rinard. 2007. Living in the comfort zone. In Proceedings of the 22nd Annual ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications, OOPSLA 2007, October 21-25, 2007, Montreal, Quebec, Canada. 611–622.

Digital Library

[62]

Martin C. Rinard, Jiasi Shen, and Varun Mangalick. 2018. Active Learning for Inference and Regeneration of Computer Programs That Store and Retrieve Data. In Proceedings of the 2018 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward! 2018). ACM, New York, NY, USA, 12–28.

Digital Library

[63]

Koushik Sen, Darko Marinov, and Gul Agha. 2005. CUTE: A Concolic Unit Testing Engine for C. In Proceedings of the 10th European Software Engineering Conference Held Jointly with 13th ACM SIGSOFT International Symposium on Foundations of Software Engineering (ESEC/FSE-13). ACM, New York, NY, USA, 263–272.

Digital Library

[64]

Burr Settles. 2009. Active Learning Literature Survey. Computer Sciences Technical Report 1648. University of Wisconsin–Madison.

[65]

Jiasi Shen and Martin Rinard. 2018. Using Dynamic Monitoring to Synthesize Models of Applications That Access Databases. Technical Report. http://hdl.handle.net/1721.1/118184

[66]

Stelios Sidiroglou-Douskos, Eric Lahtinen, Nathan Rittenhouse, Paolo Piselli, Fan Long, Deokhwan Kim, and Martin Rinard. 2015. Targeted Automatic Integer Overflow Discovery Using Goal-Directed Conditional Branch Enforcement. In Proceedings of the Twentieth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’15). ACM, New York, NY, USA, 473– 486.

Digital Library

[67]

Armando Solar-Lezama, Liviu Tancau, Rastislav Bodik, Sanjit Seshia, and Vijay Saraswat. 2006. Combinatorial Sketching for Finite Programs. In Proceedings of the 12th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS XII). ACM, New York, NY, USA, 404–415.

Digital Library

[68]

H. Tanno, X. Zhang, T. Hoshino, and K. Sen. 2015. TesMa and CATG: Automated Test Generation Tools for Models of Enterprise Applications. In 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, Vol. 2. 717–720.

Digital Library

[69]

Frits Vaandrager. 2017. Model Learning. Commun. ACM 60, 2 (Jan. 2017), 86–95.

Digital Library

[70]

Margus Veanes, Pavel Grigorenko, Peli de Halleux, and Nikolai Tillmann. 2009. Symbolic Query Exploration. In Formal Methods and Software Engineering, Karin Breitman and Ana Cavalcanti (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 49–68.

Digital Library

[71]

Margus Veanes, Nikolai Tillmann, and Jonathan de Halleux. 2010. Qex: Symbolic SQL Query Explorer. In Logic for Programming, Artificial Intelligence, and Reasoning, Edmund M. Clarke and Andrei Voronkov (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 425–446.

Digital Library

[72]

Michele Volpato and Jan Tretmans. 2015. Approximate Active Learning of Nondeterministic Input Output Transition Systems. Electronic Communications of the EASST 72 (2015).

[73]

Chenglong Wang, Alvin Cheung, and Rastislav Bodik. 2017. Synthesizing Highly Expressive SQL Queries from Input-output Examples. In Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2017). ACM, New York, NY, USA, 452–466.

Digital Library

[74]

Chenglong Wang, Alvin Cheung, and Rastislav Bodik. 2018. Speeding Up Symbolic Reasoning for Relational Queries. Proc. ACM Program. Lang. 2, OOPSLA, Article 157 (Oct. 2018), 25 pages.

Digital Library

[75]

Xinyu Wang, Isil Dillig, and Rishabh Singh. 2018. Program synthesis using abstraction refinement. PACMPL 2, POPL (2018), 63:1–63:30.

Digital Library

[76]

Michael Widenius and Davis Axmark. 2002. Mysql Reference Manual (1st ed.). O’Reilly & Associates, Inc., Sebastopol, CA, USA.

Digital Library

[77]

Wenfei Wu, Ying Zhang, and Sujata Banerjee. 2016. Automatic Synthesis of NF Models by Program Analysis. In Proceedings of the 15th ACM Workshop on Hot Topics in Networks (HotNets ’16). ACM, New York, NY, USA, 29–35.

Digital Library

[78]

Navid Yaghmazadeh, Christian Klinger, Isil Dillig, and Swarat Chaudhuri. 2016. Synthesizing transformations on hierarchically structured data. In Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2016, Santa Barbara, CA, USA, June 13-17, 2016. 508–521.

Digital Library

[79]

Navid Yaghmazadeh, Xinyu Wang, and Isil Dillig. 2018. Automated Migration of Hierarchical Data to Relational Tables Using Programmingby-example. Proc. VLDB Endow. 11, 5 (Jan. 2018), 580–593.

Digital Library

[80]

Navid Yaghmazadeh, Yuepeng Wang, Isil Dillig, and Thomas Dillig. 2017. SQLizer: Query Synthesis from Natural Language. Proc. ACM Program. Lang. 1, OOPSLA, Article 63 (Oct. 2017), 26 pages.

Digital Library

[81]

Hong Yan, David Garlan, Bradley Schmerl, Jonathan Aldrich, and Rick Kazman. 2004. DiscoTect: A System for Discovering Architectures from Running Systems. In Proceedings of the 26th International Conference on Software Engineering (ICSE ’04). IEEE Computer Society, Washington, DC, USA, 470–479.

Digital Library

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Mariano BWang ZPailoor SCollberg CDillig I(2024)Control-Flow Deobfuscation using Trace-Informed Compositional Program SynthesisProceedings of the ACM on Programming Languages10.1145/36897898:OOPSLA2(2211-2241)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689789
Wu JWei LJiang YCheung SRen LXu C(2023)Programming by Example Made EasyACM Transactions on Software Engineering and Methodology10.1145/360718533:1(1-36)Online publication date: 7-Jul-2023
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Zhang WPanda AShenker SBaumann ACrooks NSchwarzkopf M(2023)Access Control for Database Applications: Beyond Policy EnforcementProceedings of the 19th Workshop on Hot Topics in Operating Systems10.1145/3593856.3595905(223-230)Online publication date: 22-Jun-2023
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Index Terms

Using active learning to synthesize models of applications that access databases
1. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Software reverse engineering
  2. Software notations and tools
    1. Compilers
      1. Source code generation
    2. Context specific languages
      1. Domain specific languages

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cover image ACM Conferences

PLDI 2019: Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation

June 2019

1162 pages

ISBN:9781450367127

DOI:10.1145/3314221

General Chair:
Kathryn S. McKinley
Google, USA
,
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Kathleen Fisher
Tufts University, USA

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PLDI '19: 40th ACM SIGPLAN Conference on Programming Language Design and Implementation

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Cited By

Mariano BWang ZPailoor SCollberg CDillig I(2024)Control-Flow Deobfuscation using Trace-Informed Compositional Program SynthesisProceedings of the ACM on Programming Languages10.1145/36897898:OOPSLA2(2211-2241)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689789
Wu JWei LJiang YCheung SRen LXu C(2023)Programming by Example Made EasyACM Transactions on Software Engineering and Methodology10.1145/360718533:1(1-36)Online publication date: 7-Jul-2023
https://dl.acm.org/doi/10.1145/3607185
Zhang WPanda AShenker SBaumann ACrooks NSchwarzkopf M(2023)Access Control for Database Applications: Beyond Policy EnforcementProceedings of the 19th Workshop on Hot Topics in Operating Systems10.1145/3593856.3595905(223-230)Online publication date: 22-Jun-2023
https://dl.acm.org/doi/10.1145/3593856.3595905
Chawla GAman NKomondoor RBokil AKharat NDwyer MDamian DZeller A(2022)Verification of ORM-based controllers by summary inferenceProceedings of the 44th International Conference on Software Engineering10.1145/3510003.3510148(2340-2351)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510003.3510148
Vasilakis NBenetopoulos AHanda SSchoen AShen JRinard MKim YKim JVigna GShi E(2021)Supply-Chain Vulnerability Elimination via Active Learning and RegenerationProceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security10.1145/3460120.3484736(1755-1770)Online publication date: 12-Nov-2021
https://dl.acm.org/doi/10.1145/3460120.3484736
Shen JRinard M(2021)Active Learning for Inference and Regeneration of Applications that Access DatabasesACM Transactions on Programming Languages and Systems10.1145/343095242:4(1-119)Online publication date: 22-Jan-2021
https://dl.acm.org/doi/10.1145/3430952
Cito JShen JRinard M(2020)An Empirical Study on the Impact of Deimplicitization on Comprehension in Programs Using Application FrameworksProceedings of the 17th International Conference on Mining Software Repositories10.1145/3379597.3387507(598-601)Online publication date: 29-Jun-2020
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Handa SRinard MDevanbu PCohen MZimmermann T(2020)Inductive program synthesis over noisy dataProceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3368089.3409732(87-98)Online publication date: 8-Nov-2020
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An K(2020)Enhancing Web App Execution with Automated ReengineeringCompanion Proceedings of the Web Conference 202010.1145/3366424.3382087(274-278)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366424.3382087
Cambronero JDang TVasilakis NShen JWu JRinard MMasuhara HPetricek T(2019)Active learning for software engineeringProceedings of the 2019 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software10.1145/3359591.3359732(62-78)Online publication date: 23-Oct-2019
https://dl.acm.org/doi/10.1145/3359591.3359732

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