Lascad : Language-agnostic software categorization and similar application detection

Altarawy, Doaa; Shahin, Hossameldin; Mohammed, Ayat; Meng, Na

doi:10.1016/j.jss.2018.04.018

Cited by 18 publications

(12 citation statements)

References 7 publications

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“…Source Code [18,19,9,20,21,22,23,14,24] Other Project Data [25,26,10,27,28,29,30,11] (A) source code; and (B) other project data (e.g., README files), as we are interested in the classification task using semantic information, and structural (can be extracted from source code). Table 1 contains a list of the works divided by their approach.…”

Section: Data Source Workmentioning

confidence: 99%

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Antipatterns in Software Classification Taxonomies

Sas¹,

Capiluppi²

2022

Preprint

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Section: Data Source Workmentioning

confidence: 99%

“…Another unsupervised approach was adopted by LASCAD [23], a language agnostic classification and similarity tool. As in LACT, the authors used LDA over the source code, and further applied hierarchical clustering with cosine similarity on the output topic terms matrix of LDA to merge similar topics.…”

Section: Source Code Approachesmentioning

confidence: 99%

Antipatterns in Software Classification Taxonomies

Sas¹,

Capiluppi²

2022

Preprint

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“…-Information extraction (e.g., VSM) (Nguyen et al 2012;Zhang et al 2018;Chen et al 2020;Thomas et al 2013;Fowkes et al 2016); -Classification (e.g., Support Vector Machine -SVM) (Hindle et al 2013;Le et al 2017;Liu et al 2017;Demissie et al 2020;Zhao et al 2020;Shimagaki et al 2018;Gopalakrishnan et al 2017;Thomas et al 2013); -Clustering (e.g., K-means) (Jiang et al 2019;Cao et al 2017;Liu et al 2017;Zhang et al 2016;Altarawy et al 2018;Demissie et al 2020;Gorla et al 2014); -Structured prediction (e.g., Conditional Random Field -CRF) (Ahasanuzzaman et al 2019); -Artificial neural networks (e.g., Recurrent Neural Network -RNN) (Murali et al 2017;Le et al 2017); -Evolutionary algorithms (e.g., Multi-Objective Evolutionary Algorithm -MOEA) (Blasco et al 2020;Pérez et al 2018); -Web crawling (Nabli et al 2018). Pagano and Maalej (2013) was the only study that contributed an exploration that combined LDA with another text mining technique.…”

Section: Types Of Contributionmentioning

confidence: 99%

Topic modeling in software engineering research

2021

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Topic modeling using models such as Latent Dirichlet Allocation (LDA) is a text mining technique to extract human-readable semantic “topics” (i.e., word clusters) from a corpus of textual documents. In software engineering, topic modeling has been used to analyze textual data in empirical studies (e.g., to find out what developers talk about online), but also to build new techniques to support software engineering tasks (e.g., to support source code comprehension). Topic modeling needs to be applied carefully (e.g., depending on the type of textual data analyzed and modeling parameters). Our study aims at describing how topic modeling has been applied in software engineering research with a focus on four aspects: (1) which topic models and modeling techniques have been applied, (2) which textual inputs have been used for topic modeling, (3) how textual data was “prepared” (i.e., pre-processed) for topic modeling, and (4) how generated topics (i.e., word clusters) were named to give them a human-understandable meaning. We analyzed topic modeling as applied in 111 papers from ten highly-ranked software engineering venues (five journals and five conferences) published between 2009 and 2020. We found that (1) LDA and LDA-based techniques are the most frequent topic modeling techniques, (2) developer communication and bug reports have been modelled most, (3) data pre-processing and modeling parameters vary quite a bit and are often vaguely reported, and (4) manual topic naming (such as deducting names based on frequent words in a topic) is common.

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“…Zhang et al's RepoPal [45] works to detect similar repositories using key GitHub features (such as similar README files) to help facilitate actions such as source code reuse, explore related repositories, and identify plagiarism. Altarawy et al 's LASCAD [6] automatically categorizes software technologies from source code and finds similar repositories based on the categorizations. Prana et al's multilabel classifier [38] processes textual features of READMEs and classifies README file contents for improved information discovery.…”

Section: Related Workmentioning

confidence: 99%

“…Notable prior contributions to better facilitate information discovery on GitHub have included work on classifying contents of README files [38], identifying similar repositories [6,35,45], and identifying non-engineered repositories from engineered repositories [30]. However, no prior work has focused on repository descriptions as a primary concern for project discovery.…”

Section: Introductionmentioning

confidence: 99%

Generating GitHub Repository Descriptions: A Comparison of Manual and Automated Approaches

Jazlyn¹,

Jang²,

Huang³

et al. 2021

Preprint

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Given the vast number of repositories hosted on GitHub, project discovery and retrieval have become increasingly important for GitHub users. Repository descriptions serve as one of the first points of contact for users who are accessing a repository. However, repository owners often fail to provide a high-quality description; instead, they use vague terms, the purpose of the repository is poorly explained, or the description is omitted entirely. In this work, we examine the current practice of writing GitHub repository descriptions. Our investigation leads to the proposal of the LSP (Language, Software technology, and Purpose) template to formulate good descriptions for GitHub repositories that are clear, concise, and informative. To understand the extent to which current automated techniques can support generating repository descriptions, we compare the performance of state-of-the-art text summarization methods on this task. Finally, our user study with GitHub users reveals that automated summarization can adequately be used for default description generation for GitHub repositories, while the descriptions which follow the LSP template offer the most effective instrument for communicating with GitHub users.

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Lascad : Language-agnostic software categorization and similar application detection

Cited by 18 publications

References 7 publications

Antipatterns in Software Classification Taxonomies

Antipatterns in Software Classification Taxonomies

Topic modeling in software engineering research

Generating GitHub Repository Descriptions: A Comparison of Manual and Automated Approaches

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