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ASSESSING TEST DATA ADEQUACY THROUGH program inference

ACM TRANSACTIONS ON programMING LANGUAGES AND SYSTEMS 1983年第4期5卷 641-655页

作者： WEYUKER, EJ Department of Computer Science Courant Institute of Mathematical Sciences New York University 251 Mercer Street New York NY

Despite the almost universal reliance on testing as the means of locating software errors and its long history of use, few criteria have been proposed for deciding when software has been thoroughly tested. As a basis for the development of usable notions of test data adequacy, an abstract definition is proposed and examined, and approximations to this definition are considered. © 1983, ACM. All rights reserved.

关键词： inductive inference program inference program testing software testing test data adequacy

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Multi-modal program inference: A Marriage of Pre-trained Language Models and Component-Based Synthesis

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PROCEEDINGS OF THE ACM ON programMING LANGUAGES-PACMPL 2021年第OOPSLA期5卷 1–29页

作者： Rahmani, Kia Raza, Mohammad Gulwani, Sumit Le, Vu Morris, Daniel Radhakrishna, Arjun Soares, Gustavo Tiwari, Ashish Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA Microsoft Redmond WA USA

Multi-modal program synthesis refers to the task of synthesizing programs (code) from their specification given in different forms, such as a combination of natural language and examples. Examples provide a precise but incomplete specification, and natural language provides an ambiguous but more "complete" task description. Machine-learned pre-trained models (PTMs) are adept at handling ambiguous natural language, but struggle with generating syntactically and semantically precise code. program synthesis techniques can generate correct code, often even from incomplete but precise specifications, such as examples, but they are unable to work with the ambiguity of natural languages. We present an approach that combines PTMs with component-based synthesis (CBS): PTMs are used to generate candidates programs from the natural language description of the task, which are then used to guide the CBS procedure to find the program that matches the precise examples-based specification. We use our combination approach to instantiate multi-modal synthesis systems for two programming domains: the domain of regular expressions and the domain of CSS selectors. Our evaluation demonstrates the effectiveness of our domain-agnostic approach in comparison to a state-of-the-art specialized system, and the generality of our approach in providing multi-modal program synthesis from natural language and examples in different programming domains.

关键词： program inference Natural Language Models GPT-3

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Active Learning for inference and Regeneration of Applications that Access Databases

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ACM TRANSACTIONS ON programMING LANGUAGES AND SYSTEMS 2021年第4期42卷 1–119页

作者： Shen, Jiasi Rinard, Martin C. MIT EECS 32 Vassar St Cambridge MA 02139 USA CSAIL 32 Vassar St Cambridge MA 02139 USA

We present KONURE, a new system that uses active learning to infer models of applications that retrieve data from 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 contents, 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 contents, 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.

关键词： Active learning program inference program regeneration

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Supply-Chain Vulnerability Elimination via Active Learning and Regeneration 21

Supply-Chain Vulnerability Elimination via Active Learning a...

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ACM SIGSAC Conference on Computer and Communications Security (ACM CCS)

作者： Vasilakis, Nikos Benetopoulos, Achilles Handa, Shivam Schoen, Alizee Shen, Jiasi Rinard, Martin C. MIT CSAIL Cambridge MA 02139 USA UC Santa Cruz Santa Cruz CA USA

ISBN: (纸本)9781450384544

Software supply-chain attacks target components that are integrated into client applications. Such attacks often target widely-used components, with the attack taking place via operations (for example, file system or network accesses) that do not affect those aspects of component behavior that the client observes. We propose new active library learning and regeneration (ALR) techniques for inferring and regenerating the client-observable behavior of software components. Using increasingly sophisticated rounds of exploration, ALR generates inputs, provides these inputs to the component, and observes the resulting outputs to infer a model of the component's behavior as a program in a domain-specific language. We present HARP, an ALR system for string processing components. We apply HARP to successfully infer and regenerate string-processing components written in JavaScript and C/C++. Our results indicate that, in the majority of cases, HARP completes the regeneration in less than a minute, remains fully compatible with the original library, and delivers performance indistinguishable from the original library. We also demonstrate that HARP can eliminate vulnerabilities associated with libraries targeted in several highly visible security incidents, specifically event-stream, left-pad, and string-compare.

关键词： Supply-chain attacks Third-party libraries Packages Modules program inference program synthesis

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Model Transformation by Demonstration 09

Model Transformation by Demonstration

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12th International Conference on Model Driven Engineering Languages and Systems

作者： Sun, Yu White, Jules Gray, Jeff Univ Alabama Birmingham Dept Comp & Informat Sci Birmingham AL USA Vanderbilt Univ Inst Software Integrated Syst Nashville TN USA

ISBN: (纸本)9783642044243

Model transformations provide a powerful capability to automate model refinements. However, the use of model transformation languages may present challenges to those who are unfamiliar with a Specific transformation language. This paper presents an approach called model transformation by demonstration (MTBD), which allows an end-user to demonstrate the exact transformation desired by actually editing a Source model and demonstrating the changes that evolve to a target model. An inference engine built into the underlying modeling tool records all editing operations and infers a transformation pattern, which can be reused in other models. The paper motivates the need for the approach and discusses the technical contributions of MTBD. A case Study with several sample inferred transformations serves as a concrete example of the benefits of MTBD.

关键词： Model transformation program inference Refactoring

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Using Active Learning to Synthesize Models of Applications That Access Databases 2019

Using Active Learning to Synthesize Models of Applications T...

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40th ACM SIGPLAN Conference on programming Language Design and Implementation (PLDI) part of ACM's Federated Computing Research Conference (FCRC)

作者： Shen, Jiasi Rinard, Martin C. MIT EECS Cambridge MA 02139 USA MIT CSAIL Cambridge MA 02139 USA

ISBN: (纸本)9781450367127

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 contents, 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 contents, 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.

关键词： Active learning program inference program regeneration

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Active Learning for Software Engineering

Active Learning for Software Engineering

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ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on programming and Software (Onward) part of SPLASH Conference

作者： Cambronero, Jose P. Dang, Thurston H. Y. Vasilakis, Nikos Shen, Jiasi Wu, Jerry Rinard, Martin C. CSAIL MIT Cambridge MA 02139 USA Google Mountain View CA 94043 USA Univ Penn Philadelphia PA 19104 USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781450369954

Software applications have grown increasingly complex to deliver the features desired by users. Software modularity has been used as a way to mitigate the costs of developing such complex software. Active learning-based program inference provides an elegant framework that exploits this modularity to tackle development correctness, performance and cost in large applications. Inferred programs can be used for many purposes, including generation of secure code, code re-use through automatic encapsulation, adaptation to new platforms or languages, and optimization. We show through detailed examples how our approach can infer three modules in a representative application. Finally, we outline the broader paradigm and open research questions.

关键词： program inference program modeling active learning

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A Perspective on the Evolution of Live programming

A Perspective on the Evolution of Live Programming

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1st International Workshop on Live programming (LIVE)

作者： Tanimoto, Steven L. Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA

ISBN: (纸本)9781467362658

Liveness in programming environments generally refers to the ability to modify a running program. Liveness is one form of a more general class of behaviors by a programming environment that provide information to programmers about what they are constructing. This paper gives a brief historical perspective on liveness and proposes an extension of a hierarchy given in 1990, to now account for even more powerful execution-oriented tools for programmers. In addition, while liveness concerns the timeliness of execution feedback, considering a broader array of forms of feedback is helpful both in better understanding liveness and in designing ever more powerful development tools.

关键词： Liveness live programming live coding debugging software development tools liveness levels integrated development environment software engineering code completion program inference tactical prediction strategic prediction

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Constructing programs from Example Computations

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IEEE Transactions on Software Engineering 1976年第3期SE-2卷 141-153页

作者： Biermann, Alan W. Krishnaswamy, Ramachandran Department of Computer Science Computation Center Duke University Durham NC 27706 United States Department of Computer Science Ohio State University Columbus OH 43210 United States

An autoprogrammer is an interactive computer programming system which automatically constructs computer programs from example computations executed by the user. The example calculations are done in a scratch pad fashion at a computer display using a light pen or other graphic input device, and the system stores a detailed history of all of the steps executed in the process. Then the system automatically synthesizes the shortest possible program which is capable of executing the observed examples. The paper describes the computational environment provided by the system, proves that the program synthesis technique is both “sound” and “complete,” describes the design of the system, and gives some programs it was used to create. Copyright © 1976 by The Institute of Electrical and Electronics Engineers, Inc.

关键词： Automatic program synthesis autoprogramming induction techniques interactive graphics system interactive program construction program inference programming system

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Active Learning for inference and Regeneration of Computer programs That Store and Retrieve Data

Active Learning for Inference and Regeneration of Computer P...

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ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on programming and Software (Onward!)

作者： Rinard, Martin C. Shen, Jiasi Mangalick, Varun MIT EECS Cambridge MA 02139 USA MIT CSAIL Cambridge MA 02139 USA

ISBN: (纸本)9781450360319

As modern computation platforms become increasingly complex, their programming interfaces are increasingly difficult to use. This complexity is especially inappropriate given the relatively simple core functionality that many of the computations implement. We present a new approach for obtaining software that executes on modern computing platforms with complex programming interfaces. Our approach starts with a simple seed program, written in the language of the developer's choice, that implements the desired core functionality. It then systematically generates inputs and observes the resulting outputs to learn the core functionality. It finally automatically regenerates new code that implements the learned core functionality on the target computing platform. This regenerated code contains boilerplate code for the complex programming interfaces that the target computing platform presents. By providing a productive new mechanism for capturing and encapsulating knowledge about how to use modern complex interfaces, this new approach promises to greatly reduce the developer effort required to obtain secure, robust software that executes on modern computing platforms.

关键词： Active Learning program inference

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