Ruyi Ji
Yingfei Xiong
ABSTRACT
Interactive program synthesis aims to solve the ambiguity in specifications, and selecting the proper question to minimize the rounds of interactions is critical to the performance of interactive program synthesis. In this paper we address this question selection problem and propose two algorithms. SampleSy approximates a state-of-the-art strategy proposed for optimal decision tree and has a short response time to enable interaction. EpsSy further reduces the rounds of interactions by approximating SampleSy with a bounded error rate. To implement the two algorithms, we further propose VSampler, an approach to sampling programs from a probabilistic context-free grammar based on version space algebra. The evaluation shows the effectiveness of both algorithms.
- [n. d.]. Supplementary Material. https://github.com/jiry17/IntSy.Google Scholar
- Micah Adler and Brent Heeringa. 2008. Approximating optimal binary decision trees. In Approximation, Randomization and Combinatorial Optimization. Algorithms and Techniques. Springer, 1–9.Google Scholar
- 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.Google Scholar
- Syntaxguided synthesis. In Formal Methods in Computer-Aided Design, FMCAD 2013, Portland, OR, USA, October 20-23, 2013. 1–8.Google Scholar
- Rajeev Alur, Arjun Radhakrishna, and Abhishek Udupa. 2017. Scaling Enumerative Program Synthesis via Divide and Conquer. In Tools and Algorithms for the Construction and Analysis of Systems - 23rd International Conference, TACAS 2017, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2017, Uppsala, Sweden, April 22-29, 2017, Proceedings, Part I. 319–336. 10.1007/978-3-662-54577-5_18 Google ScholarCross Ref
- Shaon Barman, Rastislav Bodík, Satish Chandra, Emina Torlak, Arka Aloke Bhattacharya, and David Culler. 2015. Toward tool support for interactive synthesis. In 2015 ACM International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software, Onward! 2015, Pittsburgh, PA, USA, October 25-30, 2015. 121–136.Google ScholarDigital Library
- Nikolaj Bjørner and Anh-Dung Phan. 2014.Google Scholar
- ν Z - Maximal Satisfaction with Z3. In 6th International Symposium on Symbolic Computation in Software Science, SCSS 2014, Gammarth, La Marsa, Tunisia, December 7-8, 2014. 1–9. http://www.easychair.org/publications/paper/200953Google Scholar
- Nikolaj Bjørner, Anh-Dung Phan, and Lars Fleckenstein. 2015.Google Scholar
- ν Z - An Optimizing SMT Solver. In Tools and Algorithms for the Construction and Analysis of Systems - 21st International Conference, TACAS 2015, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2015, London, UK, April 11-18, 2015. Proceedings. 194–199. Google ScholarDigital Library
- Nader H. Bshouty. 1995. Exact Learning Boolean Functions via the Monotone Theory. Electronic Colloquium on Computational Complexity (ECCC) 2, 8 (1995). http://eccc.hpi-web.de/eccc-reports/1995/TR95- 008/index.htmlGoogle Scholar
- Nader H. Bshouty, Richard Cleve, Ricard Gavaldà, Sampath Kannan, and Christino Tamon. 1996. Oracles and Queries That Are Sufficient for Exact Learning. J. Comput. Syst. Sci. 52, 3 (1996), 421–433. Google ScholarDigital Library
- Venkatesan T Chakaravarthy, Vinayaka Pandit, Sambuddha Roy, Pranjal Awasthi, and Mukesh Mohania. 2007.Google Scholar
- Venkatesan T Chakaravarthy, Vinayaka Pandit, Sambuddha Roy, and Yogish Sabharwal. 2009. Approximating decision trees with multiway branches. In International Colloquium on Automata, Languages, and Programming. Springer, 210–221.Google Scholar
- Sanjoy Dasgupta. 2004. Analysis of a greedy active learning strategy. In Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, NIPS 2004, December 13-18, 2004, Vancouver, British Columbia, Canada]. 337–344. http://papers.nips.cc/paper/2636analysis-of-a-greedy-active-learning-strategyGoogle Scholar
- Leonardo De Moura and Nikolaj Bjørner. 2008. Z3: An efficient SMT solver. In International conference on Tools and Algorithms for the Construction and Analysis of Systems. Springer, 337–340.Google ScholarDigital Library
- Yu Feng, Ruben Martins, Osbert Bastani, and Isil Dillig. 2018.Google Scholar
- 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.Google Scholar
- 420–435.Google Scholar
- Yu Feng, Ruben Martins, Jacob Van Geffen, Isil Dillig, and Swarat Chaudhuri. [n. d.]. Component-based synthesis of table consolidation and transformation tasks from examples. In Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Question Selection for Interactive Program Synthesis PLDI ’20, June 15–20, 2020, London, UK Implementation, PLDI 2017, Barcelona, Spain, June 18-23, 2017.Google ScholarDigital Library
- Joel Galenson, Philip Reames, Rastislav Bodik, Bjorn Hartmann, and Koushik Sen. 2014. CodeHint: dynamic and interactive synthesis of code snippets. In 36th International Conference on Software Engineering, ICSE ’14, Hyderabad, India - May 31 - June 07, 2014. 653–663.Google ScholarDigital Library
- Sumit Gulwani. 2011. Automating string processing in spreadsheets using input-output examples. In Proceedings of the 38th ACM SIGPLANSIGACT Symposium on Principles of Programming Languages, POPL 2011, Austin, TX, USA, January 26-28, 2011. 317–330.Google ScholarDigital Library
- Sumit Gulwani and Prateek Jain. 2017. Programming by Examples: PL meets ML. In APLAS.Google Scholar
- Sumit Gulwani, Susmit Jha, Ashish Tiwari, and Ramarathnam Venkatesan. 2011. Synthesis of loop-free programs. In Proceedings of the 32nd ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2011, San Jose, CA, USA, June 4-8, 2011. 62–73.Google ScholarDigital Library
- Sumit Gulwani, Oleksandr Polozov, and Rishabh Singh. 2017. Program Synthesis. Foundations and Trends in Programming Languages 4, 1-2 (2017), 1–119.Google ScholarCross Ref
- Anupam Gupta, Viswanath Nagarajan, and R Ravi. 2017. Approximation algorithms for optimal decision trees and adaptive TSP problems. Mathematics of Operations Research 42, 3 (2017), 876–896.Google ScholarDigital Library
- Tihomir Gvero, Viktor Kuncak, and Ruzica Piskac. 2011. Interactive Synthesis of Code Snippets. In Computer Aided Verification - 23rd International Conference, CAV 2011, Snowbird, UT, USA, July 14-20, 2011. Proceedings. 418–423.Google Scholar
- Susmit Jha, Sumit Gulwani, Sanjit A. Seshia, and Ashish Tiwari. 2010.Google Scholar
- Oracle-guided component-based program synthesis. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1, ICSE 2010, Cape Town, South Africa, 1-8 May 2010. 215–224.Google Scholar
- Susmit Jha and Sanjit A. Seshia. 2017. A theory of formal synthesis via inductive learning. Acta Inf. 54, 7 (2017), 693–726.Google ScholarDigital Library
- Mark Johnson. 1998. PCFG Models of Linguistic Tree Representations. Computational Linguistics 24, 4 (1998), 613–632.Google ScholarDigital Library
- Sean Kandel, Andreas Paepcke, Joseph M. Hellerstein, and Jeffrey Heer. 2011. Wrangler: interactive visual specification of data transformation scripts. In Proceedings of the International Conference on Human Factors in Computing Systems, CHI 2011, Vancouver, BC, Canada, May 7-12, 2011. 3363–3372.Google ScholarDigital Library
- Daniel Larraz, Albert Oliveras, Enric Rodríguez-Carbonell, and Albert Rubio. 2014. Minimal-Model-Guided Approaches to Solving Polynomial Constraints and Extensions. In Theory and Applications of Satisfiability Testing - SAT 2014 - 17th International Conference, Held as Part of the Vienna Summer of Logic, VSL 2014, Vienna, Austria, July 14-17, 2014. Proceedings. 333–350. Google ScholarCross Ref
- Hyafil Laurent and Ronald L Rivest. 1976. Constructing optimal binary decision trees is NP-complete. Information processing letters 5, 1 (1976), 15–17.Google Scholar
- Vu Le and Sumit Gulwani. 2014. FlashExtract: a framework for data extraction by examples. In ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI ’14, Edinburgh, United Kingdom - June 09 - 11, 2014. 542–553.Google ScholarDigital Library
- Vu Le, Daniel Perelman, Oleksandr Polozov, Mohammad Raza, Abhishek Udupa, and Sumit Gulwani. 2017. Interactive Program Synthesis. CoRR abs/1703.03539 (2017).Google Scholar
- Woosuk Lee, Kihong Heo, Rajeev Alur, and Mayur Naik. 2018. Accelerating search-based program synthesis using learned probabilistic models. In Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2018, Philadelphia, PA, USA, June 18-22, 2018. 436–449. 3192366.3192410 Google ScholarDigital Library
- Alan Leung, John Sarracino, and Sorin Lerner. 2015. Interactive parser synthesis by example. In Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation, Portland, OR, USA, June 15-17, 2015. 565–574.Google ScholarDigital Library
- Yi Li, Aws Albarghouthi, Zachary Kincaid, Arie Gurfinkel, and Marsha Chechik. 2014. Symbolic optimization with SMT solvers. In The 41st Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL ’14, San Diego, CA, USA, January 20-21, 2014.Google ScholarDigital Library
- Fan Long and Martin Rinard. 2016.Google Scholar
- Automatic patch generation by learning correct code. In Proceedings of the 43rd Annual ACM SIGPLANSIGACT Symposium on Principles of Programming Languages. 298–312. Google ScholarDigital Library
- Ruben Martins, Jia Chen, Yanju Chen, Yu Feng, and Isil Dillig. 2019.Google Scholar
- Trinity: An Extensible Synthesis Framework for Data Science. PVLDB 12, 12 (2019), 1914–1917.Google Scholar
- Mikaël Mayer, Gustavo Soares, Maxim Grechkin, Vu Le, Mark Marron, Oleksandr Polozov, Rishabh Singh, Benjamin G. Zorn, and Sumit Gulwani. 2015. User Interaction Models for Disambiguation in Programming by Example. In UIST. ACM, 291–301.Google Scholar
- Sergey Mechtaev, Alberto Griggio, Alessandro Cimatti, and Abhik Roychoudhury. 2018. Symbolic execution with existential second-order constraints. In Proceedings of the 2018 ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/SIGSOFT FSE 2018, Lake Buena Vista, FL, USA, November 04-09, 2018. 389–399. Google ScholarDigital Library
- 3236049Google Scholar
- Hong Mei and Lu Zhang. 2018.Google Scholar
- Can big data bring a breakthrough for software automation? Sci. China Inf. Sci. 61, 5 (2018), 056101:1– 056101:3. Google ScholarCross Ref
- Saswat Padhi, Prateek Jain, Daniel Perelman, Oleksandr Polozov, Sumit Gulwani, and Todd D. Millstein. 2018. FlashProfile: a framework for synthesizing data profiles. PACMPL 2, OOPSLA (2018), 150:1–150:28.Google ScholarDigital Library
- Phitchaya Mangpo Phothilimthana, Aditya Thakur, Rastislav Bodík, and Dinakar Dhurjati. 2016.Google Scholar
- Scaling up Superoptimization. In Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS ’16, Atlanta, GA, USA, April 2-6, 2016. 297–310. 2872362.2872387 Google ScholarDigital Library
- Oleksandr Polozov and Sumit Gulwani. 2015.Google Scholar
- FlashMeta: a framework for inductive program synthesis. In Proceedings of the 2015 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications, OOPSLA 2015, part of SPLASH 2015, Pittsburgh, PA, USA, October 25-30, 2015. 107–126.Google Scholar
- Zichao Qi, Fan Long, Sara Achour, and Martin Rinard. 2015. An Analysis of Patch Plausibility and Correctness for Generate-and-validate Patch Generation Systems (ISSTA 2015). 24–36.Google Scholar
- Greg Schohn and David Cohn. 2000. Less is More: Active Learning with Support Vector Machines. In Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29 - July 2, 2000. 839–846.Google Scholar
- Roberto Sebastiani and Patrick Trentin. 2015. OptiMathSAT: A Tool for Optimization Modulo Theories. In Computer Aided Verification - 27th International Conference, CAV 2015, San Francisco, CA, USA, July 18-24, 2015, Proceedings, Part I. 447–454. 3-319-21690-4_27 Google ScholarCross Ref
- Roberto Sebastiani and Patrick Trentin. 2015.Google Scholar
- Pushing the Envelope of Optimization Modulo Theories with Linear-Arithmetic Cost Functions. In Tools and Algorithms for the Construction and Analysis of Systems - 21st International Conference, TACAS 2015, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2015, London, UK, April 11-18, 2015. Proceedings. 335–349. Google ScholarDigital Library
- Armando Solar-Lezama, Rodric M. Rabbah, Rastislav Bodík, and Kemal Ebcioglu. 2005. Programming by sketching for bit-streaming programs. In Proceedings of the ACM SIGPLAN 2005 Conference on Programming Language Design and Implementation, Chicago, IL, USA, June 12-15, 2005. 281–294. PLDI ’20, June 15–20, 2020, London, UK Ruyi Ji, Jingjing Liang, Yingfei Xiong, Lu Zhang, and Zhenjiang HuGoogle ScholarDigital Library
- Armando Solar-Lezama, Liviu Tancau, Rastislav BodÃŋk, Sanjit A. Seshia, and Vijay A. Saraswat. 2006. Combinatorial sketching for finite programs. ACM SIGOPS Operating Systems Review 40, 5 (2006), 404.Google ScholarDigital Library
- Armando Solarlezama. 2008. Program synthesis by sketching. Dissertations & Theses - Gradworks (2008).Google Scholar
- Chenglong Wang, Alvin Cheung, and Rastislav Bodík. 2017. Interactive Query Synthesis from Input-Output Examples. In Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD Conference 2017, Chicago, IL, USA, May 14-19, 2017. 1631–1634.Google ScholarDigital Library
- Yuepeng Wang, Xinyu Wang, and Isil Dillig. 2018. Relational program synthesis. PACMPL 2, OOPSLA (2018), 155:1–155:27.Google Scholar
- Yingfei Xiong, Jie Wang, Runfa Yan, Jiachen Zhang, Shi Han, Gang Huang, and Lu Zhang. 2017. Precise Condition Synthesis for Program Repair. In Proceedings of the 39th International Conference on Software Engineering, ICSE 2017, Buenos Aires, Argentina, May 20-28, 2017.Google ScholarDigital Library
Index Terms
- Question selection for interactive program synthesis
Recommendations
Algorithmic program synthesis: introduction
Program synthesis is a process of producing an executable program from a specification. Algorithmic synthesis produces the program automatically, without an intervention from an expert. While classical compilation falls under the definition of ...
LooPy: interactive program synthesis with control structures
One vision for program synthesis, and specifically for programming by example (PBE), is an interactive programmer's assistant, integrated into the development environment. To make program synthesis practical for interactive use, prior work on Small-Step ...
Can reactive synthesis and syntax-guided synthesis be friends?
SPLASH Companion 2021: Companion Proceedings of the 2021 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for HumanityWhile reactive synthesis and syntax-guided synthesis (SyGuS) have seen enormous progress in recent years, combining the two approaches has remained a challenge. In this work, we present the synthesis of reactive programs from Temporal Stream Logic ...
Comments