A survey on goal-oriented visualization of clone data

Basit, Hamid Abdul; Hammad, Muhammad; Koschke, Rainer

doi:10.1109/vissoft.2015.7332414

Cited by 10 publications

(3 citation statements)

References 28 publications

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“… Abid (2019) have previously used clone methods for code recommendation, however, the approach used a different similarity measure based on API calls for recommending relevant clone methods. Clones are generally considered to be harmful for a software system, and mainly researchers work on techniques for avoiding and eliminating clones ( Yoshida et al, 2019 ; Wang & Godfrey, 2014 ; Basit et al, 2015 ; Basit, Hammad & Koschke, 2015 ; Hammad et al, 2020b ). Clone refactoring recommendation systems have been developed for this purpose.…”

Section: Related Workmentioning

confidence: 99%

Clone-advisor: recommending code tokens and clone methods with deep learning and information retrieval

Hammad

Babur

Basit

et al. 2021

PeerJ Computer Science

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Software developers frequently reuse source code from repositories as it saves development time and effort. Code clones (similar code fragments) accumulated in these repositories represent often repeated functionalities and are candidates for reuse in an exploratory or rapid development. To facilitate code clone reuse, we previously presented DeepClone, a novel deep learning approach for modeling code clones along with non-cloned code to predict the next set of tokens (possibly a complete clone method body) based on the code written so far. The probabilistic nature of language modeling, however, can lead to code output with minor syntax or logic errors. To resolve this, we propose a novel approach called Clone-Advisor. We apply an information retrieval technique on top of DeepClone output to recommend real clone methods closely matching the predicted clone method, thus improving the original output by DeepClone. In this paper we have discussed and refined our previous work on DeepClone in much more detail. Moreover, we have quantitatively evaluated the performance and effectiveness of Clone-Advisor in clone method recommendation.

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Section: Related Workmentioning

confidence: 99%

Clone-advisor: recommending code tokens and clone methods with deep learning and information retrieval

Hammad

Babur

Basit

et al. 2021

PeerJ Computer Science

Self Cite

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“…According to Roy and Cordy [68], around 5 to 50% of the code in so ware applications can be enclosed in clones, which can be of several granularity levels such as line, method, le, and directory. Most of the time clones are considered to be harmful for a so ware system, and mainly researchers work on techniques for avoiding and eliminating clones [10,11,88,93]. However, Kapser and Godfrey [40] observe that code clones are not always bad, as they can also have positive e ects on the so ware development in certain circumstances.…”

Section: Introductionmentioning

confidence: 99%

DeepClone: Modeling Clones to Generate Code Predictions

Hammad¹,

Babur²,

Basit³

et al. 2020

Preprint

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During so ware development, programmers o en tend to reuse the code for common functionalities, available in other source code repositories. is activity helps them to reduce time and e ort to develop code, instead of building it from scratch. Code clones are candidates for reuse in an exploratory or rapid development, as they represent o en repeated functionality in so ware systems. To facilitate code clone reuse, we propose a novel approach, Deep-Clone, where we utilize a deep learning algorithm for modeling code clones and predicting the next possible set of tokens (up to the cloned method body) based on the user input so far. e generated predictions aim to potentially help developers to write code rapidly with minimum tuning of values later on. DeepClone applies natural language processing techniques to learn from a large code corpus (the BigCloneBench dataset), and generates code tokens (full clone methods where applicable) using the model learned. We have quantitatively evaluated our solution to assess (1) our model's quality and its accuracy in token prediction, and (2) its performance and e ectiveness in clone method prediction. With a high quality and accurate model as the foundation, we further discuss scenarios for exploiting our approach. CCS CONCEPTS•Computing methodologies → Neural networks; •So ware and its engineering → Language features; Reusability; Automatic programming; So ware development methods;

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“…In order to distinguish the visualization types under investigation look at the following tables and figures. While Figure 1 represents visualizations encountered in everyday life (which are also the main types used by accounting professionals in the past and in the further course of this paper defined as type I visualizations), Figure 2 represents new and more complex visualization types to be used in an interactive manner especially designed to handle large structured and unstructured data sets which are henceforth called type II visualizations (Basit et al, 2015;Borkin et al, 2013;Grammel et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Interactive visualization of big data in the field of accounting

Perkhofer

Hofer

Walchshofer

et al. 2019

JAAR

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Purpose Big Data introduces high amounts and new forms of structured, unstructured and semi-structured data into the field of accounting and this requires alternative data management and reporting methods. Generating insights from these new data sources highlight the need for different and interactive forms of visualization in the field of visual analytics. Nonetheless, a considerable gap between the recommendations in research and the current usage in practice is evident. In order to understand and overcome this gap, a detailed analysis of the status quo as well as the identification of potential barriers for adoption is vital. The paper aims to discuss this issue. Design/methodology/approach A survey with 145 business accountants from Austrian companies from a wide array of business sectors and all hierarchy levels has been conducted. The survey is targeted toward the purpose of this study: identifying barriers, clustered as human-related and technological-related, as well as investigating current practice with respect to interactive visualization use for Big Data. Findings The lack of knowledge and experience regarding new visualization types and interaction techniques and the sole focus on Microsoft Excel as a visualization tool can be identified as the main barriers, while the use of multiple data sources and the gradual implementation of further software tools determine the first drivers of adoption. Research limitations/implications Due to the data collection with a standardized survey, there was no possibility of dealing with participants individually, which could lead to a misinterpretation of the given answers. Further, the sample population is Austrian, which might cause issues in terms of generalizing results to other geographical or cultural heritages. Practical implications The study shows that those knowledgeable and familiar with interactive Big Data visualizations indicate high perceived ease of use. It is, therefore, necessary to offer sufficient training as well as user-centered visualizations and technological support to further increase usage within the accounting profession. Originality/value A lot of research has been dedicated to the introduction of novel forms of interactive visualizations. However, little focus has been laid on the impact of these new tools for Big Data from a practitioner’s perspective and their needs.

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A survey on goal-oriented visualization of clone data

Cited by 10 publications

References 28 publications

Clone-advisor: recommending code tokens and clone methods with deep learning and information retrieval

Clone-advisor: recommending code tokens and clone methods with deep learning and information retrieval

DeepClone: Modeling Clones to Generate Code Predictions

Interactive visualization of big data in the field of accounting

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