Visualizing the Usage of Pythonic Idioms Over Time: A Case Study of the with open Idiom

Sakulniwat, Tattiya; Ragkhitwetsagul, Chaiyong; Choetkiertikul, Morakot; Kula, Raula Gaikovina; Wang, Dong; Ishio, Takashi; Matsumoto, Kenichi

doi:10.1109/iwesep49350.2019.00016

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…The non-Pythonic code snippets in the Py/NPy database are stored in Siamese's code search index (using Elasticsearch). Then, Teddy connects with GitHub through Smee.io 4 , a webhook service, and uses the code changes in a pull request's commits as Siamese's search queries. The queries are matched with the NPys in the code search index using the idiom matching Detection mode.…”

Section: Methodsmentioning

confidence: 99%

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Teddy: Automatic Recommendation of Pythonic Idiom Usage For Pull-Based Software Projects

Phan-udom¹,

Wattanakul²,

Sakulniwat³

et al. 2020

Preprint

Self Cite

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Pythonic code is idiomatic code that follows guiding principles and practices within the Python community. Offering performance and readability benefits, Pythonic code is claimed to be widely adopted by experienced Python developers, but can be a learning curve to novice programmers. To aid with Pythonic learning, we create an automated tool, called Teddy, that can help checking the Pythonic idiom usage. The tool offers a prevention mode with Just-In-Time analysis to recommend the use of Pythonic idiom during code review and a detection mode with historical analysis to run a thorough scan of idiomatic and non-idiomatic code. In this paper, we first describe our tool and an evaluation of its performance. Furthermore, we present a case study that demonstrates how to use Teddy in a real-life scenario on an Open Source project. An evaluation shows that Teddy has high precision for detecting Pythonic idiom and non-Pythonic code. Using interactive visualizations, we demonstrate how novice programmers can navigate and identify Pythonic idiom and non-Pythonic code in their projects. Our video demo with the full interactive visualizations is available at https://youtu.be/vOCQReSvBxA.

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Section: Methodsmentioning

confidence: 99%

“…Compared to non-Pythonic code, Pythonic idioms are well-accepted form of Python programming [3] due to many benefits. For example, Sakulniwat et al [4] showed that adopting the with open Pythonic idiom gives benefits not only in terms of readability, but also prevents memory leaks by automatically closing the file after finishing the task.…”

Section: Introductionmentioning

confidence: 99%

Teddy: Automatic Recommendation of Pythonic Idiom Usage For Pull-Based Software Projects

Phan-udom¹,

Wattanakul²,

Sakulniwat³

et al. 2020

Preprint

Self Cite

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“…Hence, PYEVOLVE missed cases where it did not match the generalization of the rule. However, developers evolve Python in a Pythonic way [56], [57], and they evolve idioms mostly in the same way. Hence, despite learning from one example, PYEVOLVE achieved a high precision and recall.…”

Section: A Rq1: What Is the Effectiveness Of Pyevolve In Generating C...mentioning

confidence: 99%

“…Alexandru et al [56] present a nonexhaustive list of Python idioms gleaned from a developer survey. Sakulniwat et al [57] studied the evolution of Python with statements over time, while Wang et al [62] studied Python code smells. However, none of these fully automate code transformations.…”

Section: Related Workmentioning

confidence: 99%

PYEVOLVE: Automating Frequent Code Changes in Python ML Systems

Dilhara

Dig

Ketkar

2023

2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)

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Because of the naturalness of software and the rapid evolution of Machine Learning (ML) techniques, frequently repeated code change patterns (CPATs) occur often. They range from simple API migrations to changes involving several complex control structures such as for loops. While manually performing CPATs is tedious, the current state-of-the-art techniques for inferring transformation rules are not advanced enough to handle unseen variants of complex CPATs, resulting in a low recall rate. In this paper we present a novel, automated workflow that mines CPATs, infers the transformation rules, and then transplants them automatically to new target sites. We designed, implemented, evaluated and released this in a tool, PYEVOLVE. At its core is a novel data-flow, control-flow aware transformation rule inference engine. Our technique allows us to advance the state-of-the-art for transformation-by-example tools; without it, 70% of the code changes that PYEVOLVE transforms would not be possible to automate. Our thorough empirical evaluation of over 40,000 transformations shows 97% precision and 94% recall. By accepting 90% of CPATs generated by PYEVOLVE in famous open-source projects, developers confirmed its changes are useful.

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“…Researchers have only begun to study how developers use Pythonic idioms. Recent work is now concerned with the different aspects of Pythonic detection and usage [4,7,8,10,13].…”

Section: Introductionmentioning

confidence: 99%

Does Coding in Pythonic Zen Peak Performance? Preliminary Experiments of Nine Pythonic Idioms at Scale

Leelaprute,

Chinthanet,

Wattanakriengkrai

et al. 2021

Self Cite

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In the field of data science, and for academics in general, the Python programming language is a popular choice, mainly because of its libraries for storing, manipulating, and gaining insight from data. Evidence includes the versatile set of machine learning, data visualization, and manipulation packages used for the evergrowing size of available data. The Zen of Python is a set of guiding design principles that developers use to write acceptable and elegant Python code. Most principles revolve around simplicity. However, as the need to compute large amounts of data, performance has become a necessity for the Python programmer. The new idea in this paper is to confirm whether writing the Pythonic way peaks performance at scale. As a starting point, we conduct a set of preliminary experiments to evaluate nine Pythonic code examples by comparing the performance of both Pythonic and Non-Pythonic code snippets. Our results reveal that writing in Pythonic idioms may save memory and time. We show that incorporating list comprehension, generator expression, zip, and itertools.zip_longest idioms can save up to 7,000 MB and up to 32.25 seconds. The results open more questions on how they could be utilized in a real-world setting. The replication package includes all scripts, and the results are available at

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Visualizing the Usage of Pythonic Idioms Over Time: A Case Study of the with open Idiom

Cited by 10 publications

References 9 publications

Teddy: Automatic Recommendation of Pythonic Idiom Usage For Pull-Based Software Projects

Teddy: Automatic Recommendation of Pythonic Idiom Usage For Pull-Based Software Projects

PYEVOLVE: Automating Frequent Code Changes in Python ML Systems

Does Coding in Pythonic Zen Peak Performance? Preliminary Experiments of Nine Pythonic Idioms at Scale

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