Automatic Android Deprecated-API Usage Update by Learning from Single Updated Example

Haryono, Stefanus Agus; Thung, Ferdian; Kang, Hong Jin; Serrano, Lucas; Muller, Gilles; Lawall, Julia; Lo, David; Jiang, Lingxiao

doi:10.1145/3387904.3389285

Cited by 28 publications

(8 citation statements)

References 21 publications

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“…AppEvolve [9] automates API-usage update for Android, creating patches from before-and after-update code examples. CocciEvolve [10] infers a readable transformation in the form of the semantic patch language (SmPL) [17] using only a single after-update code example. NEAT [11] generates transformation rules for deprecated Android API usage migrations without any update example.…”

Section: B Scenario II -Context-dependent Api Migrationmentioning

confidence: 99%

MLCatchUp: Automated Update of Deprecated Machine-Learning APIs in Python

Haryono¹,

Ferdian²,

David³

et al. 2021

Preprint

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Machine learning (ML) libraries are gaining vast popularity, especially in the Python programming language. Using the latest version of such libraries is recommended to ensure the best performance and security. When migrating to the latest version of a machine learning library, usages of deprecated APIs need to be updated, which is a time-consuming process. In this paper, we propose MLCatchUp, an automated API usage update tool for deprecated APIs of popular ML libraries written in Python. MLCatchUp automatically infers the required transformation to migrate usages of deprecated API through the differences between the deprecated and updated API signatures. MLCatchUp offers a readable transformation rule in the form of a domain specific language (DSL). We evaluate MLCatchUp using a dataset of 267 real-world Python code containing 551 usages of 68 distinct deprecated APIs, where MLCatchUp achieves 90.7% accuracy. A video demonstration of MLCatchUp is available at https://youtu.be/5NjOPNt5iaA.

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Section: B Scenario II -Context-dependent Api Migrationmentioning

confidence: 99%

MLCatchUp: Automated Update of Deprecated Machine-Learning APIs in Python

Haryono¹,

Ferdian²,

David³

et al. 2021

Preprint

Self Cite

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“…In the automated analysis, we automatically check whether the updated code produces any syntax error by compiling the updated code using the Python built-in py_compile module. 8 In the manual analysis, we employed the help of three Python programmers with at least four years of experience to label whether the updated code is correct. These programmers are non-authors, and were given instruction and training before the labelling process.…”

Section: Experiments Settingmentioning

confidence: 99%

“…AppEvolve [7] transforms the usage of deprecated Android APIs into backward-compatible updated code by learning from code examples. CocciEvolve [8] provides improvement from App-Evolve by creating readable transformations using only a single update example. Thung et al [9] proposed NEAT, a tool to generate transformation rules that can assist developers in deprecated API replacement for Android APIs.…”

Section: Related Workmentioning

confidence: 99%

Characterization and Automatic Updates of Deprecated Machine-Learning API Usages

Haryono¹,

Ferdian²,

David³

et al. 2021

Preprint

Self Cite

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Due to the rise of AI applications, machine learning (ML) libraries, often written in Python, have become far more accessible. ML libraries tend to be updated periodically, which may deprecate existing APIs, making it necessary for application developers to update their usages. In this paper, we build a tool to automate deprecated API usage updates. We first present an empirical study to better understand how updates of deprecated ML API usages in Python can be done. The study involves a dataset of 112 deprecated APIs from Scikit-Learn, TensorFlow, and PyTorch. Guided by the findings of our empirical study, we propose MLCatchUp, a tool to automate the updates of Python deprecated API usages, that automatically infers the API migration transformation through comparison of the deprecated and updated API signatures. These transformations are expressed in a Domain Specific Language (DSL). We evaluate MLCatchUp using a dataset containing 267 files with 551 API usages that we collected from public GitHub repositories. In our dataset, MLCatchUp can detect deprecated API usages with perfect accuracy, and update them correctly for 80.6% of the cases. We further improve the accuracy of MLCatchUp in performing updates by adding a feature that allows it to accept an additional user input that specifies the transformation constraints in the DSL for context-dependent API migration. Using this addition, MLCatchUp can make correct updates for 90.7% of the cases.

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“…Several approaches have been proposed in the literature for other ecosystems to migrate from deprecated APIs [20], [9], [8]. Yaoguo Xi et al [20] proposed an approach and built a tool DAAMT to migrate from deprecated APIs in Java to their replacements if recorded in the documentation.…”

Section: Related Workmentioning

confidence: 99%

“…Ko et al have analyzed the quality of documentation for resolving deprecated APIs [7]. Researchers have proposed techniques to automatically update deprecated APIs [8], [9]. However, most of them are for static programming languages such as Java, C# and Android SDKs.…”

Section: Introductionmentioning

confidence: 99%

APIScanner -- Towards Automated Detection of Deprecated APIs in Python Libraries

Vadlamani¹,

Kalicheti²,

Chimalakonda³

2021

Preprint

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Python libraries are widely used for machine learning and scientific computing tasks today. APIs in Python libraries are deprecated due to feature enhancements and bug fixes in the same way as in other languages. These deprecated APIs are discouraged from being used in further software development. Manually detecting and replacing deprecated APIs is a tedious and time-consuming task due to the large number of API calls used in the projects. Moreover, the lack of proper documentation for these deprecated APIs makes the task challenging. To address this challenge, we propose an algorithm and a tool APIScanner that automatically detects deprecated APIs in Python libraries. This algorithm parses the source code of the libraries using abstract syntax tree (ASTs) and identifies the deprecated APIs via decorator, hard-coded warning or comments. APIScanner is a Visual Studio Code Extension that highlights and warns the developer on the use of deprecated API elements while writing the source code. The tool can help developers to avoid using deprecated API elements without the execution of code. We tested our algorithm and tool on six popular Python libraries, which detected 838 of 871 deprecated API elements. Demo of APIScanner: https://youtu.be/1hy ugf-iek. Documentation, tool, and source code can be found here: https://rishitha957.github.io/APIScanner.

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Automatic Android Deprecated-API Usage Update by Learning from Single Updated Example

Cited by 28 publications

References 21 publications

MLCatchUp: Automated Update of Deprecated Machine-Learning APIs in Python

MLCatchUp: Automated Update of Deprecated Machine-Learning APIs in Python

Characterization and Automatic Updates of Deprecated Machine-Learning API Usages

APIScanner -- Towards Automated Detection of Deprecated APIs in Python Libraries

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