Summarization of Software Artifacts : A Review

Gupta, Som; S.K, Gupta

doi:10.5121/ijcsit.2017.9512

Cited by 5 publications

(6 citation statements)

References 52 publications

(136 reference statements)

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“…The percentage increase of the works in this area is too much if we consider other techniques as well. • Research in the field of extractive summarization [62] has reached the saturation stage after 2013 but now the research is going on in the field of abstractive summarization. • Research in the field of sentiment analysis is increasing year by year.…”

Section: Discussion and Analysismentioning

confidence: 99%

“…Summarization is of two types: extractive and abstractive. Extractive summarization [61,62] is to extract the important sentences from the document and arrange them in the way they appear in the original document, whereas abstractive summarization [62] is to select the important phrases or sentences and then arrange them in meaningful order by applying linguistic rules. NLP plays an important role in the abstractive summarization as it requires the knowledge about the syntax, semantics and context and this is possible from NLP techniques only [61,63].…”

Section: Summarizationmentioning

confidence: 99%

“…and Java languages. More work on performing summarization for other object-oriented languages is required [62].…”

Section: Summarization Techniquesmentioning

confidence: 99%

“…More efforts on extending it are required [97]. More work on creating personalized summaries based on the role of persons is required [62].…”

Section: Summarization Techniquesmentioning

confidence: 99%

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Natural language processing in mining unstructured data from software repositories: a review

Gupta

2019

Sādhanā

Self Cite

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With the increasing popularity of open-source platforms, software data is easily available from various open-source tools like GitHub, CVS, SVN, etc. More than 80 percent of the data present in them is unstructured. Mining data from these repositories helps project managers, developers and businesses, in getting interesting insights. Most of the software artefacts present in these repositories are in the natural language form, which makes natural language processing (NLP) an important part of mining to get the useful results. The paper reviews the application of NLP techniques in the field of Mining Software Repositories (MSR). The paper mainly focuses on sentiment analysis, summarization, traceability, norms mining and mobile analytics. The paper presents the major NLP works performed in this area by surveying the research papers from 2000 to 2018. The paper firstly describes the major artefacts present in the software repositories where the NLP techniques have been applied. Next, the paper presents some popular open-source NLP tools that have been used to perform NLP tasks. Later the paper discusses, in brief, the research state of NLP in MSR field. The paper also lists down the various challenges along with the pointers for future work in this field of research and finally the conclusion.

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Section: Discussion and Analysismentioning

confidence: 99%

Section: Summarizationmentioning

confidence: 99%

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Natural language processing in mining unstructured data from software repositories: a review

Gupta

2019

Sādhanā

Self Cite

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“…Researchers such as Corradetti et al [17] employed drones to capture photographs of cliffs composed of nearly vertical outcrops, creating 3D models that were used for crack analysis and understanding crack propagation patterns. Similarly, Sharad et al [18] used drones to capture high-resolution images of complex and hazardous landslides, generating cm-level accuracy 3D models. Javier et al [19] employed drones to create highly accurate and high-resolution 3D models for identifying and interpreting ancient Roman gold mining sites in Northwestern Spain, revealing areas such as excavation sites, canals, reservoirs, and drainage channels.…”

Section: Related Studiesmentioning

confidence: 99%

OutcropHyBNet: Hybrid Backbone Networks with Data Augmentation for Accurate Stratum Semantic Segmentation of Monocular Outcrop Images in Carbon Capture and Storage Applications

Madokoro,

Sato,

Nix

et al. 2023

Sensors

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The rapid advancement of climate change and global warming have widespread impacts on society, including ecosystems, water security, food production, health, and infrastructure. To achieve significant global emission reductions, approximately 74% is expected to come from cutting carbon dioxide (CO2) emissions in energy supply and demand. Carbon Capture and Storage (CCS) has attained global recognition as a preeminent approach for the mitigation of atmospheric carbon dioxide levels, primarily by means of capturing and storing CO2 emissions originating from fossil fuel systems. Currently, geological models for storage location determination in CCS rely on limited sampling data from borehole surveys, which poses accuracy challenges. To tackle this challenge, our research project focuses on analyzing exposed rock formations, known as outcrops, with the goal of identifying the most effective backbone networks for classifying various strata types in outcrop images. We leverage deep learning-based outcrop semantic segmentation techniques using hybrid backbone networks, named OutcropHyBNet, to achieve accurate and efficient lithological classification, while considering texture features and without compromising computational efficiency. We conducted accuracy comparisons using publicly available benchmark datasets, as well as an original dataset expanded through random sampling of 13 outcrop images obtained using a stationary camera, installed on the ground. Additionally, we evaluated the efficacy of data augmentation through image synthesis using Only Adversarial Supervision for Semantic Image Synthesis (OASIS). Evaluation experiments on two public benchmark datasets revealed insights into the classification characteristics of different classes. The results demonstrate the superiority of Convolutional Neural Networks (CNNs), specifically DeepLabv3, and Vision Transformers (ViTs), particularly SegFormer, under specific conditions. These findings contribute to advancing accurate lithological classification in geological studies using deep learning methodologies. In the evaluation experiments conducted on ground-level images obtained using a stationary camera and aerial images captured using a drone, we successfully demonstrated the superior performance of SegFormer across all categories.

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Overview of Mining Software Repositories

Luzgin

Kholod

2020

2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus)

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Summarization of Software Artifacts : A Review

Cited by 5 publications

References 52 publications

Natural language processing in mining unstructured data from software repositories: a review

Natural language processing in mining unstructured data from software repositories: a review

OutcropHyBNet: Hybrid Backbone Networks with Data Augmentation for Accurate Stratum Semantic Segmentation of Monocular Outcrop Images in Carbon Capture and Storage Applications

Overview of Mining Software Repositories

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