Understanding performance concerns in the API documentation of data science libraries

Tao, Yida; Jiang, Jiefang; Liu, Yepang; Xu, Zhiwu; Qin, Shengchao

doi:10.1145/3324884.3416543

Cited by 6 publications

(3 citation statements)

References 44 publications

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“…Maintainability. Maintainability includes studies that are related to the process of correcting errors or conflicts found in API documentations (e.g., [21,42,43,56,61,71,72,73,74,81,83,86,104,105,106,107]). The improvement of API documentation maintenance has been explored in two areas.…”

Section: Quality Dimensions For Api Documentationmentioning

confidence: 99%

Scout-bot: Leveraging API Community Knowledge for Exploration and Discovery of API Learning Resources

Ajam

Rodríguez

Benatallah

2021

CLEIej

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Application Programming Interface (API) is a core technology that facilitates developers’ productivity by enabling the reuse of software components. Understanding APIs and gaining knowledge about their usage are therefore fundamental needs for developers. Here, API documentation plays a pivotal role in enabling developers to take full advantage of the benefits brought by APIs. The quality of API documentation has therefore become an important concern given the celerity and dynamics at which APIs are now being made available to users. This article aims at exploring existing research in the area of API documentation in order to identify the associated quality dimensions addressed by the literature. The research is carried out as a systematic mapping study where 103 research papers selected from the literature were reviewed and a total of 5 core quality dimensions were identified and analyzed. By focusing on the two most relevant quality dimensions (understandability and completeness), this article presents an approach to enable API users to explore, discover and learn about APIs through API topic issues discussed in Stack Overflow (SO). We demonstrate the feasibility of our approach through Scout-bot, our tool for exploration and discovery of API topic issues.

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Section: Quality Dimensions For Api Documentationmentioning

confidence: 99%

Scout-bot: Leveraging API Community Knowledge for Exploration and Discovery of API Learning Resources

Ajam

Rodríguez

Benatallah

2021

CLEIej

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“…2) Solver: This parameter has five solvers which are lbfgs, liblinear, sag, saga, and newton-cg. Liblinear is a decent choice for small datasets, while sag and saga are quicker for big ones [38]. Only lbfgs, sag, newton-cg, and saga can handle multinomial loss in multiclass issues.…”

Section: ) Penaltymentioning

confidence: 99%

Low Time Complexity Model for Email Spam Detection using Logistic Regression

Mrisho¹,

Ndibwile²,

Sam³

2021

IJACSA

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Spam emails have recently become a concern on the Internet. Machine learning techniques such as Neural Networks, Naïve Bayes, and Decision Trees have frequently been used to combat these spam emails. Despite their efficiency, time complexity in high-dimensional datasets remains a significant challenge. Due to a large number of features in high-dimensional datasets, the intricacy of this problem grows exponentially. The existing approaches suffer from a computational burden when thousands of features are used (high-time complexity). To reduce time complexity and improve accuracy in high-dimensional datasets, extra steps of feature selection and parameter tuning are necessary. This work recommends the use of a hybrid logistic regression model with a feature selection approach and parameter tuning that could effectively handle a big dimensional dataset. The model employs the Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction method to mitigate the drawbacks of Term Frequency (TF) to obtain an equal feature weight. Using publicly available datasets (Enron and Lingspam), we compared the model's performance to that of other contemporary models. The proposed model achieved a low level of time complexity while maintaining a high level of spam detection rate of 99.1%.

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“…Overall, a blockchain-based semantic exchange framework for Web 3.0 can facilitate the development of a participatory economy by enabling decentralized, peer-to-peer transactions without intermediaries. [4] This can help to reduce transaction costs, increase transparency, and enable greater participation in economic activities.…”

Section: Introductionmentioning

confidence: 99%