Tortoise: Interactive system configuration repair

Weiss, Aaron; Guha, Arjun; Brun, Yuriy

doi:10.1109/ase.2017.8115673

Cited by 30 publications

(15 citation statements)

References 52 publications

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“…Existing work has been performed on the automatic repair of environment configurations. Weiss et al presented Tortoise, a system which synthesizes patches for configuration scripts, but can only do so by analyzing recorded developer actions [31]. Horton and Parnin work on inferring working environment configurations without the need of developer interaction, but they crucially ignore dependency versions [9].…”

Section: Related Workmentioning

confidence: 99%

V2: Fast Detection of Configuration Drift in Python

Horton

Parnin

2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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Code snippets are prevalent, but are hard to reuse because they often lack an accompanying environment configuration. Most are not actively maintained, allowing for drift between the most recent possible configuration and the code snippet as the snippet becomes out-of-date over time. Recent work has identified the problem of validating and detecting out-of-date code snippets as the most important consideration for code reuse. However, determining if a snippet is correct, but simply out-of-date, is a non-trivial task. In the best case, breaking changes are well documented, allowing developers to manually determine when a code snippet contains an out-of-date API usage. In the worst case, determining if and when a breaking change was made requires an exhaustive search through previous dependency versions.We present V2, a strategy for determining if a code snippet is out-of-date by detecting discrete instances of configuration drift, where the snippet uses an API which has since undergone a breaking change. Each instance of configuration drift is classified by a failure encountered during validation and a configuration patch, consisting of dependency version changes, which fixes the underlying fault. V2 uses feedback-directed search to explore the possible configuration space for a code snippet, reducing the number of potential environment configurations that need to be validated. When run on a corpus of public Python snippets from prior research, V2 identifies 248 instances of configuration drift.

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Section: Related Workmentioning

confidence: 99%

V2: Fast Detection of Configuration Drift in Python

Horton

Parnin

2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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“…Their work is similar to ours in that it incorporates knowledge from existing, developer driven sources and uses that information to infer a correct environment specification codified in a build script. Other approaches to configuration repair target inconsistencies between file systems and configuration scripts [24] or capture and replay developer changes [8]. Macho et al focus on repairing Java projects using the Maven build system by updating dependency versions, deleting listed dependencies, or explicitly specifying common repositories [25].…”

Section: Related Workmentioning

confidence: 99%

“…We implement DockerizeMe, a tool for applying dependency resolution to a code snippet and generating a Dockerfile for the corresponding environment. Unlike other approaches to automated software configuration, which focus on repairing configuration errors ( [8], [9], [10]), DockerizeMe focuses on inferring a complete configuration without external inputs. Being able to automatically infer code dependencies has the potential to save developers time, reduce costs of learning and development, and enable repair and verification of code snippets in online platforms.…”

Section: Introductionmentioning

confidence: 99%

DockerizeMe: Automatic Inference of Environment Dependencies for Python Code Snippets

Horton

Parnin

2019

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)

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Platforms like Stack Overflow and GitHub's gist system promote the sharing of ideas and programming techniques via the distribution of code snippets designed to illustrate particular tasks. Python, a popular and fast-growing programming language, sees heavy use on both sites, with nearly one million questions asked on Stack Overflow and 400 thousand public gists on GitHub. Unfortunately, around 75% of the Python example code shared through these sites cannot be directly executed. When run in a clean environment, over 50% of public Python gists fail due to an import error for a missing library.We present DockerizeMe, a technique for inferring the dependencies needed to execute a Python code snippet without import error. DockerizeMe starts with offline knowledge acquisition of the resources and dependencies for popular Python packages from the Python Package Index (PyPI). It then builds Docker specifications using a graph-based inference procedure. Our inference procedure resolves import errors in 892 out of nearly 3,000 gists from the Gistable dataset for which Gistable's baseline approach could not find and install all dependencies.

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“…Configuration parameters are common in many software systems, and represent a large source of error across disciplines [3,4,5]. In robotics, behaviors often rely on environment-dependent parameters for robust and accurate execution.…”

Section: Related Workmentioning

confidence: 99%

“…In domains that are not amenable to unit-testing user input can be used as a specification for correction behavior. For example, Tortoise [12] propagates complete system configuration fixes from a user shell to a system configuration specification.…”

Section: Related Workmentioning

confidence: 99%

Interactive Robot Transition Repair With SMT

Holtz

Guha

Biswas

2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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Complex robot behaviors are often structured as state machines, where states encapsulate actions and a transition function switches between states. Since transitions depend on physical parameters, when the environment changes, a roboticist has to painstakingly readjust the parameters to work in the new environment. We present interactive SMTbased Robot Transition Repair (SRTR): instead of manually adjusting parameters, we ask the roboticist to identify a few instances where the robot is in a wrong state and what the right state should be. An automated analysis of the transition function 1) identifies adjustable parameters, 2) converts the transition function into a system of logical constraints, and 3) formulates the constraints and user-supplied corrections as a MaxSMT problem that yields new parameter values. We show that SRTR finds new parameters 1) quickly, 2) with few corrections, and 3) that the parameters generalize to new scenarios. We also show that a SRTR-corrected state machine can outperform a more complex, expert-tuned state machine.

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Tortoise: Interactive system configuration repair

Cited by 30 publications

References 52 publications

V2: Fast Detection of Configuration Drift in Python

V2: Fast Detection of Configuration Drift in Python

DockerizeMe: Automatic Inference of Environment Dependencies for Python Code Snippets

Interactive Robot Transition Repair With SMT

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