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Using active learning to synthesize models of applications that access databases

Authors:

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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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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Published: 08 June 2019

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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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