A survey on the use of topic models when mining software repositories

Chen, Tse-Hsun; Thomas, Stephen W.; Hassan, Ahmed E.

doi:10.1007/s10664-015-9402-8

Cited by 163 publications

(117 citation statements)

References 219 publications

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“…Using topic models, topics are extracted from documents and are used to represent the corpora. A topic is a collection of terms that co-occur frequently in the documents of the corpus, so the documents can be clustered by topics and the entire corpus can be indexed and organized in terms of this discovered semantic structure [7], [13]. Latent Dirichlet Allocation (LDA) is a popular probabilistic topic model [21].…”

Section: B Topic Modelingmentioning

confidence: 99%

“…Before topic modeling, several preprocess steps are generally taken to reduce noise and improve the modeling results [13]. Compared to natural language text, Hindle et al [22] reported that text extracted from source code is much more repetitive and predictable.…”

Section: Preprocessing Proceduresmentioning

confidence: 99%

“…Researchers have used topic modeling to support varied software engineering tasks, including traceability link recovery [9], concept/feature location [10], source code metrics [11], and many other tasks [12], [13], however, the application of using topic modeling to support interactive program comprehension has not got as much attention. Also, there are gaps between knowing and doing when applying topic modeling to software engineering practice.…”

Section: Introductionmentioning

confidence: 99%

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JSEA: A Program Comprehension Tool Adopting LDA-based Topic Modeling

Wang¹,

Liu²

2017

ijacsa

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Abstract-Understanding a large number of source code is a big challenge for software development teams in software maintenance process. Using topic models is a promising way to automatically discover feature and structure from textual software assets, and thus support developers comprehending programs on software maintenance. To explore the application of applying topic modeling to software engineering practice, we proposed JSEA (Java Software Engineers Assistant), an interactive program comprehension tool adopting LDA-based topic modeling, to support developers during performing software maintenance tasks. JSEA utilizes essential information automatically generated from Java source code to establish a project overview and to bring search capability for software engineers. The results of our preliminary experimentation suggest the practicality of JSEA.

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Section: B Topic Modelingmentioning

confidence: 99%

Section: Preprocessing Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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JSEA: A Program Comprehension Tool Adopting LDA-based Topic Modeling

Wang¹,

Liu²

2017

ijacsa

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show abstract

“…It also has a strong tendency for overfitting, and of even greater consequence, the model is unable to generalize topic mixtures onto previously unseen documents (not part of the training data) [2], [5]. Through correcting these problems with a truly generative model, LDA has seen a surge in popularity and has acted like a springboard for numerous other advancements in IR.…”

Section: A Topic Models In Briefmentioning

confidence: 99%

“…Often, the models are treated as "black box" approaches without regard for the underlying assumptions they are based on. Parameter tuning can prove difficult without a full understanding of the specific technique to be employed [2]. Additionally, the emerging topics are by no means guaranteed to be sensible to a human reader -motivating the use of human knowledge and user interaction as an additional step toward more coherent and sensible results [3].…”

Section: Introductionmentioning

confidence: 99%

A Survey On Interactivity in Topic Models

Kjellin¹,

Yan²

2016

ijacsa

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Abstract-Trying to make sense and gain deeper insight from large sets of data is becoming a task very central to computer science in general. Topic models, capable of uncovering the semantic themes pervading through large collections of documents, have seen a surge in popularity in recent years. However, topic models are high level statistical tools; their output is given in terms of probability distributions, suited neither for simple interpretation nor deep analysis. Interpreting the fitted topic models in an intuitive manner requires visual and interactive tools. Additionally, some measure of human interaction is typically required for refining the output offered by such models. In the research, this area remains relatively unexplored -only recently has this aspect been receiving more attention. In this paper, the literature is surveyed as it pertains to interactivity and visualisation within the context of topic models, with the goal of finding current research trends in this area.

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Exploring adolescents' occupational possible selves: The role of gender and socioeconomic status

Martin

et al. 2023

The Career Development Quart

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Adolescence is an important stage in forming one's identity and developing career inspirations. The current empirical study aims to investigate gender and socioeconomic status (SES) differences in adolescents’ occupational possible selves. A total of 3336 sixth‐ to eighth‐grade students participated in the survey research and reported (1) hoped‐for occupation, (2) desire for the hoped‐for occupation, (3) likelihood of attaining the hoped‐for occupation, and (4) whether and what actions were taken toward the hoped‐for occupation. A series of regression models and advanced natural language processing techniques were applied to the survey data. Results revealed evident gender differences and some nuanced SES differences in the named occupational possible selves with a significant interaction between gender and SES on adolescents’ career‐related actions. However, this study did not find gender or SES differences in adolescents’ desire to pursue their occupational possible selves.

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A survey on the use of topic models when mining software repositories

Cited by 163 publications

References 219 publications

JSEA: A Program Comprehension Tool Adopting LDA-based Topic Modeling

JSEA: A Program Comprehension Tool Adopting LDA-based Topic Modeling

A Survey On Interactivity in Topic Models

Exploring adolescents' occupational possible selves: The role of gender and socioeconomic status

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