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Automated repair by example for firewalls

FORMAL METHODS IN SYSTEM DESIGN 2020年第1-3期56卷 127-153页

作者： Hallahan, William T. Zhai, Ennan Piskac, Ruzica Yale Univ Dept Comp Sci New Haven CT 06511 USA Alibaba Grp Seattle WA USA

Firewalls are widely deployed to manage enterprise networks. Because enterprise-scale firewalls contain hundreds or thousands of rules, ensuring the correctness of firewalls-that the rules in the firewalls meet the specifications of their administrators-is an important but challenging problem. Although existing firewall diagnosis and verification techniques can identify potentially faulty rules, they offer administrators little or no help with automatically fixing faulty rules. This paper presents FireMason, the first effort that offers automated repair by example for firewalls. Once an administrator observes undesired behavior in a firewall, she may provide input/output examples that comply with the intended behaviors. Based on the examples, FireMason automatically synthesizes new firewall rules for the existing firewall. This new firewall correctly handles packets specified by the examples, while maintaining the rest of the behaviors of the original firewall. Through a conversion of the firewalls to SMT formulas, we offer formal guarantees that the change is correct. Our evaluation results from real-world case studies show that FireMason can efficiently find repairs.

关键词： Synthesis Firewall repair programming by example Network configuration

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On the Fly Synthesis of Edit Suggestions

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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2019年第OOPSLA期3卷 1–29页

作者： Miltner, Anders Gulwani, Sumit Le, Vu Leung, Alan Radhakrishna, Arjun Soares, Gustavo Tiwari, Ashish Udupa, Abhishek Princeton Univ Princeton NJ 08544 USA Microsoft Redmond WA USA

When working with a document, users often perform context-specific repetitive edits - changes to the document that are similar but specific to the contexts at their locations. programming by demon-stration/examples (PBD/PBE) systems automate these tasks by learning programs to perform the repetitive edits from demonstration or examples. However, PBD/PBE systems are not widely adopted, mainly because they require modal UIs - users must enter a special mode to give the demonstration/examples. This paper presents BLUE-PENCIL, a modeless system for synthesizing edit suggestions on the fly. BLUE-PENCIL observes users as they make changes to the document, silently identifies repetitive changes, and automatically suggests transformations that can apply at other locations. BLUE-PENCIL is parameterized - it allows the "plug-and-play" of different PBE engines to support different document types and different kinds of transformations. We demonstrate this parameterization by instantiating BLUE-PENCIL to several domains - C# and SQL code, markdown documents, and spreadsheets - using various existing PBE engines. Our evaluation on 37 code editing sessions shows that BLUE-PENCIL synthesized edit suggestions with a precision of 0.89 and a recall of 1.0, and took 199 ms to return suggestions on average. Finally, we report on several improvements based on feedback gleaned from a field study with professional programmers to investigate the use of BLUE-PENCIL during long code editing sessions. BLUE-PENCIL has been integrated with Visual Studio IntelliCode to power the IntelliCode refactorings feature.

关键词： Program transformation Refactoring Program synthesis programming by example

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Inductive Program Synthesis via Iterative Forward-Backward Abstract Interpretation

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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2023年第PLDI期7卷 1657-1681页

作者： Yoon, Yongho Lee, Woosuk Yi, Kwangkeun Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea Hanyang Univ Dept Comp Sci & Engn Seoul South Korea

A key challenge in example-based program synthesis is the gigantic search space of programs. To address this challenge, various work proposed to use abstract interpretation to prune the search space. However, most of existing approaches have focused only on forward abstract interpretation, and thus cannot fully exploit the power of abstract interpretation. In this paper, we propose a novel approach to inductive program synthesis via iterative forward-backward abstract interpretation. The forward abstract interpretation computes possible outputs of a program given inputs, while the backward abstract interpretation computes possible inputs of a program given outputs. By iteratively performing the two abstract interpretations in an alternating fashion, we can effectively determine if any completion of each partial program as a candidate can satisfy the input-output examples. We apply our approach to a standard formulation, syntax-guided synthesis (SyGuS), thereby supporting a wide range of inductive synthesis tasks. We have implemented our approach and evaluated it on a set of benchmarks from the prior work. The experimental results show that our approach significantly outperforms the state-of-the-art approaches thanks to the sophisticated abstract interpretation techniques.

关键词： Program Synthesis programming by example Abstract Interpretation

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Integrating learning supports into the design of visual programming systems

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JOURNAL OF VISUAL LANGUAGES AND COMPUTING 2001年第5期12卷 501-524页

作者： DiGiano, C Kahn, K Cypher, A Smith, DC SRI Int Ctr Technol Learning Menlo Pk CA 94025 USA Animated Programs San Carlos CA 94070 USA Stagecast Software Inc Burlingame CA 94010 USA

The success of a programming system depends as much on the learnability of its language concepts as the usability of its interface. We argue that learnability can be significantly improved by integrating into the programming system learning supports that allow individuals to educate themselves about the syntax, semantics and applications of a language. Reflecting on our experience with developing novice programming systems, we identify infrastructural characteristics of such systems that can make the integration of learning supports practical. We focus on five core facilities: annotatability, scriptability monitorability, supplementability and constrainability. Our hope is that our examination of these technical facilities and their tradeoffs can inform the design of future programming systems that better address the educational needs of their users. (C) 2001 Academic Press.

关键词： programming systems learning instructional design end-user programming visual programming programming by example

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APIFIX: Output-Oriented Program Synthesis for Combating Breaking Changes in Libraries

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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2021年第OOPSLA期5卷 1–27页

作者： Gao, Xiang Radhakrishna, Arjun Soares, Gustavo Shariffdeen, Ridwan Gulwani, Sumit Roychoudhury, Abhik Natl Univ Singapore Singapore Singapore Microsoft Redmond WA USA

Use of third-party libraries is extremely common in application software. The libraries evolve to accommodate new features or mitigate security vulnerabilities, thereby breaking the Application programming Interface (API) used by the software. Such breaking changes in the libraries may discourage client code from using the new library versions thereby keeping the application vulnerable and not up-to-date. We propose a novel output-oriented program synthesis algorithm to automate API usage adaptations via program transformation. Our aim is not only to rely on the few example human adaptations of the clients from the old library version to the new library version, since this can lead to over-fitting transformation rules. Instead, we also rely on example usages of the new updated library in clients, which provide valuable context for synthesizing and applying the transformation rules. Our tool APIFIX provides an automated mechanism to transform application code using the old library versions to code using the new library versions - thereby achieving automated API usage adaptation to fix the effect of breaking changes. Our evaluation shows that the transformation rules inferred by APIFIX achieve 98.7% precision and 91.5% recall. By comparing our approach to state-of-the-art program synthesis approaches, we show that our approach significantly reduces over-fitting while synthesizing transformation rules for API usage adaptations.

关键词： API usage adaptation Breaking changes Program transformation Program synthesis programming by example

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Inductive Synthesis of Structurally Recursive Functional Programs from Non-recursive Expressions

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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2023年第POPL期7卷 2048-2078页

作者： Lee, Woosuk Cho, Hangyeol Hanyang Univ Dept Comp Sci & Engn Seoul South Korea

We present a novel approach to synthesizing recursive functional programs from input-output examples. Synthesizing a recursive function is challenging because recursive subexpressions should be constructed while the target function has not been fully defined yet. We address this challenge by using a new technique we call block-based pruning. A block refers to a recursion- and conditional-free expression (i.e., straight-line code) that yields an output from a particular input. We first synthesize as many blocks as possible for each input-output example, and then we explore the space of recursive programs, pruning candidates that are inconsistent with the blocks. Our method is based on an efficient version space learning, thereby effectively dealing with a possibly enormous number of blocks. In addition, we present a method that uses sampled input-output behaviors of library functions to enable a goal-directed search for a recursive program using the library. We have implemented our approach in a system called Trio and evaluated it on synthesis tasks from prior work and on new tasks. Our experiments show that Trio outperforms prior work by synthesizing a solution to 98% of the benchmarks in our benchmark suite.

关键词： programming by example Recursive Functional Programs Synthesis

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Robust Relational Layout Synthesis from examples for Android

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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2018年第OOPSLA期2卷 1–29页

作者： Bielik, Pavol Fischer, Marc Vechev, Martin Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

We present a novel approach for synthesizing robust relational layouts from examples. Given an application design consisting of a set of views and their location on the screen, we synthesize a relational layout that when rendered, places the components at that same location. We present an end-to-end system, called INFERUI, that addresses the above challenge in the context of Android. The system is based on the following technical contributions: (i) a formalization of the latest and most efficient Const raintLayout class, capturing a rich set of relational constraints, (ii) a set of robustness properties designed to prevent common layout generalization errors, (iii) a synthesis algorithm that produces relational layouts that generalize across multiple screen sizes and resolutions, and (iv) a probabilistic model of constraints that guides the synthesizer towards layouts preferred by developers. Our evaluation shows that INFERUI is practically effective: it successfully synthesizes real world complex layouts obtained from top 500 GitHub and top 500 Google Play Store applications, succeeds in 100% of the cases when synthesizing layouts for a single device, and correctly generalizes 92% of the views across multiple devices, all without requiring additional specifications.

关键词： Program synthesis programming by example Relational layouts Probabilistic models User interface design User interface errors

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Feedback-Driven Semi-supervised Synthesis of Program Transformations

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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2020年第OOPSLA期4卷 1–30页

作者： Gao, Xiang Barke, Shraddha Radhakrishna, Arjun Soares, Gustavo Gulwani, Sumit Leung, Alan Nagappan, Nachiappan Tiwari, Ashish Natl Univ Singapore Singapore Singapore Univ Calif San Diego San Diego CA 92103 USA Microsoft Redmond WA USA

While editing code, it is common for developers to make multiple related repeated edits that are all instances of a more general program transformation. Since this process can be tedious and error-prone, we study the problem of automatically learning program transformations from past edits, which can then be used to predict future edits. We take a novel view of the problem as a semi-supervised learning problem: apart from the concrete edits that are instances of the general transformation, the learning procedure also exploits access to additional inputs (program subtrees) that are marked as positive or negative depending on whether the transformation applies on those inputs. We present a procedure to solve the semi-supervised transformation learning problem using anti-unification and programming-by-example synthesis technology. To eliminate reliance on access to marked additional inputs, we generalize the semi-supervised learning procedure to a feedback-driven procedure that also generates the marked additional inputs in an iterative loop. We apply these ideas to build and evaluate three applications that use different mechanisms for generating feedback. Compared to existing tools that learn program transformations from edits, our feedback-driven semi-supervised approach is vastly more effective in successfully predicting edits with significantly lesser amounts of past edit data.

关键词： Program transformation Refactoring Program synthesis programming by example

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Training agents to recognize text by example

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AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS 2001年第1-2期4卷 79-92页

作者： Lieberman, H Nardi, BA Wright, DJ MIT Media Lab Cambridge MA 02139 USA AT&T Labs W Menlo Park CA USA Apple Comp Inc Cupertino CA 95014 USA

An important function of an agent is to be "on the lookout" for bits of information that are interesting to its user, even if these items appear in the midst of a larger body of unstructured information. But how to tell these agents which patterns are meaningful and what to do with the result? Especially when agents are used to recognize text, they are usually driven by parsers which require input in the form of textual grammar rules. Editing grammars is difficult and error-prone for end users. Grammar ["Grammars by example"] is the first direct manipulation interface designed to allow nonexpert users to define grammars interactively. The user presents concrete examples of text that he or she would like the agent to recognize. Rules are constructed by an iterative process, where Grammex heuristically parses the example, displays a set of hypotheses, and the user critiques the system's suggestions. Actions to take upon recognition are also demonstrated by example.

关键词： agents parsers grammars programming by example programming by demonstration

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Generalizable Synthesis through Unification

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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2021年第OOPSLA期5卷 1–28页

作者： Ji, Ruyi Xia, Jingtao Xiong, Yingfei Hu, Zhenjiang Peking Univ Key Lab High Confidence Software Technol Minist Educ Dept Comp Sci & TechnolEECS Beijing Peoples R China

The generalizability of PBE solvers is the key to the empirical synthesis performance. Despite the importance of generalizability, related studies on PBE solvers are still limited. In theory, few existing solvers provide theoretical guarantees on generalizability, and in practice, there is a lack of PBE solvers with satisfactory generalizability on important domains such as conditional linear integer arithmetic (CLIA). In this paper, we adopt a concept from the computational learning theory, Occam learning, and perform a comprehensive study on the framework of synthesis through unification (STUN), a state-of-the-art framework for synthesizing programs with nested if-then-else operators. We prove that Eusolver, a state-of-the-art STUN solver, does not satisfy the condition of Occam learning, and then we design a novel STUN solver, PolyGen, of which the generalizability is theoretically guaranteed by Occam learning. We evaluate PolyGen on the domains of CLIA and demonstrate that PolyGen significantly outperforms two state-of-the-art PBE solvers on CLIA, Eusolver and Euphony, on both generalizability and efficiency.

关键词： programming by example Synthesis through Unification Occam Learning

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