Enriching API Documentation with Code Samples and Usage Scenarios from Crowd Knowledge

Zhang, Jingxuan; Jiang, He; Zhang, Ren; Zhang, Tao; Huang, Zhiqiu

doi:10.1109/tse.2019.2919304

Cited by 26 publications

(31 citation statements)

References 44 publications

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“…In these circumstances, the ordinary classification algorithm tends to recognize defects as normal codes, which is far from enough to achieve a good recognition effect. [48][49][50][51][52][53] In order to solve this problem, researchers have proposed different models, among which the common strategies can be divided into two parts, data-and algorithm-based models.…”

Section: Software Defect Predictionmentioning

confidence: 99%

Software defect prediction with imbalanced distribution by radius‐synthetic minority over‐sampling technique

Guo

Dong

et al. 2021

J Software Evolu Process

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Software defect prediction, which can identify the defect-prone modules, is an effective technology to ensure the quality of software products. Due to the importance in software maintenance, many learning-based software defect prediction models are presented in recent years. Actually, the defects usually occupy a very small proportions in software source codes; thus, the imbalanced distributions between defectprone modules and non-defect-prone modules increase the learning difficulty of the classification task. To address this issue, we present a random over-sampling mechanism used to generate minority-class samples from high-dimensional sampling space to deal with the imbalanced distributions in software defect prediction, in which two constraints are applied to provide a robust way to generate new synthetic samples, that is, scaling the random over-sampling scope to a reasonable area and distinguishing the majority-class samples in a critical region. Based on nine open datasets of software projects, we experimentally verify that our presented method is effective on predict the defect-prone modules, and the effect is superior to the traditional imbalanced processing methods.

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Section: Software Defect Predictionmentioning

confidence: 99%

Software defect prediction with imbalanced distribution by radius‐synthetic minority over‐sampling technique

Guo

Dong

et al. 2021

J Software Evolu Process

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“…Many researchers have proposed many methods based on machine learning techniques for developer recommendation 31–40 …”

Section: Related Workmentioning

confidence: 99%

Toward more accurate developer recommendation via inference of development activities from interaction with bug repair process

Wang

et al. 2021

J Software Evolu Process

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Software projects usually receive a large number of submitted bug reports every day. Manually triaging the bug reports is often time‐consuming and error‐prone; thus, it is necessary to automatically assign the bug reports to the suitable developers for bug repair, with the help of bug tracking systems. Aiming to reducing the time consumption and mismatch of bug report assignments, we present a developer recommendation model for bug repair based on weighted recurrent neural network, namely, DTPM, which contains two parts: One obtains multisource semantic information of bug reports and fuses them into high‐dimensional semantic feature vectors, and the other combines a penalty matrix into a single hidden layer neural network to obtain more reasonable developer recommendations. We conduct experiments on five datasets of open bug repositories (NetBeans, OpenOffice, GCC, Mozilla, and Eclipse), and the experimental results show that DTPM can achieve better performance than state‐of‐the‐art models LDA_KL, LDA_KL, LDA_SVM, DERTOM, DREX, and DeepTriage.

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“…A pesquisa de Menezes e colegas [Menezes et al 2019] realiza um estudo exploratório, buscando esclarecer aspectos encontradas em code samples, bem como definindo um conjunto de lições apreendidas. No trabalho de Zhang e colegas [Zhang et al 2019],é proposta uma abordagem para o enriquecimento da documentação de API, mapeando code samples em cenários de uso correspondentes. Apesar dos trabalhos citados focarem em code samples, eles não abordam aspectos dos desenvolvedores que buscam ou usam esses projetos.…”

Section: Trabalhos Relacionadosunclassified

“…Apesar da crescente importância, existem poucos trabalhos na literatura técnica sobre code samples. Por exemplo, há estudos que exploram as características mais comuns de code samples e projetos similares [Menezes et al 2019, Zhang et al 2019. Nesses trabalhos, destaca-se, por exemplo, a simplicidade e baixa complexidade do código disponibilizado.…”

Section: Introductionunclassified

Quero lhe usar! Uma Análise do Público Alvo de Code Samples

Braga

Menezes

Fontão

et al. 2020

Anais Do VIII Workshop De Visualização, Evolução E Manutenção De Software (VEM 2020)

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Code samples são projetos disponibilizados por organizações para auxiliar no aprendizado de funcionalidades de seus produtos. Existe um crescimento na importância dos code samples para a comunidade de desenvolvedores, mas pouco se sabe sobre quem os usam. Acredita-se que o público-alvo dos code samples é o desenvolvedor inexperiente, mas para iniciar uma caracterização, este trabalho apresenta um estudo empírico por meio da análise de estatísticas de desenvolvedores no Stack Overflow. Como resultado, foi gerado um conjunto de implicações que pode auxiliar as organizações a manter os code samples relevantes para os desenvolvedores.

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Enriching API Documentation with Code Samples and Usage Scenarios from Crowd Knowledge

Cited by 26 publications

References 44 publications

Software defect prediction with imbalanced distribution by radius‐synthetic minority over‐sampling technique

Software defect prediction with imbalanced distribution by radius‐synthetic minority over‐sampling technique

Toward more accurate developer recommendation via inference of development activities from interaction with bug repair process

Quero lhe usar! Uma Análise do Público Alvo de Code Samples

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