A Systematic Mapping Study on Analysis of Code Repositories

Sayago-Heredia, Jaime; Pérez‐Castillo, Ricardo; Piattini, Mario

doi:10.15388/21-infor454

Cited by 7 publications

(4 citation statements)

References 97 publications

(119 reference statements)

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“…Firstly, the pretrained CNN models are compared with the proposed model in order to validate its flexibility and performance. The various pretrained CNN models used are AlexNet [29], GoogleNet [30], ResNet [31] and VggNet [32,33]. The comparison is drawn in terms of time intricacy and accuracy which are shown in Figure 8.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

An Optimized Deep Learning based Technique for Grading and Extraction of Diabetic Retinopathy Severities

Zhang

Luo²,

Cengiz

2021

IJCAI

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The prognosis of Diabetic Retinopathy (DR) requires regular eye examinations, as ophthalmologists depends on fundus segmentation to treat DR pathologies. Automated approaches for detection, segmentation and classification have developed as an imperative area of research for the effective diagnosis of DR for the treatment of serious eye conditions that prevent visual impairment. Diagnosis of various DR lesions, as well as different severities, helping the ophthalmologists to analyze variations in fundus images and take the necessary measures before the disease progresses. Deep learning techniques have evolved as a recent advent to combat the issues of conventional machine leaning based methods. An optimized deep learning framework is proposed in this article for grading and extraction of diabetic retinopathy severities. This involves various steps like background segmentation, feature set extraction, feature optimization using Cuckoo search and Convolutional Neural Network (CNN) severity grade classification. The method was validated on two standard datasets MESSIDOR and IDRiD. The proposed method yields an accuracy value of 97.55%, cross entropy loss of 0.367 and time intricacy of 20 mins and 15 secs for MESSIDOR and 98.02% cross entropy loss of 0.345 and time intricacy of 22 mins and 21 secs for IDRiD dataset; respectively. The state-of-the-art comparison depicts that the proposed CNN based method provides a maximum accuracy improvement of 10.46% comparative to the existing methodology. The proposed framework yields better accuracy by procurement of the investigative outcomes acquired exhibits proficient DR determination.Povzetek: Opisana je metoda ugotavljanja sladkorne bolezni iz slik očesnega ozadje na osnovi strojnega in globokega učenja.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

An Optimized Deep Learning based Technique for Grading and Extraction of Diabetic Retinopathy Severities

Zhang

Luo²,

Cengiz

2021

IJCAI

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“…The fifth study, "A Systematic Mapping Study on Analysis of Code Repositories" by [12], explores the analysis of code repositories to improve software maintenance and understand software evolution. While both studies utilize systematic mapping, they differ in their research objectives and the specific areas within software development they investigate.…”

Section: Related Workmentioning

confidence: 99%

“…Numerous research investigations have been conducted in the software engineering field, covering a wide range of subjects such as recommendation systems, metrics, team composition, labor market analysis, code repository evaluation, and requirement engineering complexities [8,9,10,11,12,13,14]. One of the studies [8] focused on recommendation systems specifically tailored for requirements engineering, provides valuable guidance for professionals in that field.…”

Section: Introductionmentioning

confidence: 99%

Aligning technical knowledge to an industry domain in global software development: a systematic mapping

Campos,

David,

Stroele

et al. 2024

Preprint

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Finding software developers with expertise in specific technologies that align with industry domains is an increasingly critical requirement. However, due to the ever-changing nature of the technology industry, locating these professionals has become a significant challenge for companies and institutions. This research presents a comprehensive overview of studies exploring suitable recommendation systems that can assist companies in addressing this pressing need. To conduct this study, we employ a hybrid systematic mapping approach with an initial number of 1251 studies and a final selection of 21 studies. Our work focuses on collecting data on key technologies, methodologies, and data sets utilized in proposed recommendation systems, to design a new recommendation system that can effectively identify specialists capable of aligning specific technical knowledge with industry domains. The outcomes of this study include insights into the current research trends in this field, alongside a practical overview of considerations necessary for developing a recommendation system that successfully meets the criteria for aligning technical skills with industry domains. By following a hybrid Systematic Mapping methodology and presenting the outcomes in the form of insights, this research addresses the challenge of finding software developers with domain-specific expertise in a rapidly changing technology industry, laying the groundwork for aligning technical skills with industry domains.

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“…The following details are provided by the medical staff to the investigators [31]. The muscle, leg injured, injury severity based on lay off time from soccer and whether it was a recurrence and total time for full training and competition resuming.…”

Section: Injury Surveillancementioning

confidence: 99%

Attribute Reduction Algorithm Based Early Warning Model of Sports Injury

Wang

Guo

2021

IJCAI

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With the continuous development of track and field sports, a relatively complete and scientific system has been formed in terms of training, and more attention has been paid to the planned training of teenagers for many years, psychological training and recovery training, and strengthening medical supervision and scientific research. In order to speed up the scientific training of track and field and improve the level of track and field training in China, in view of the characteristics of track and field injuries such as multiple causes and complexity, this paper analyzes and studies the attribute reduction algorithm based on attribute reduction algorithm, and proposes a sports injury early warning model based on mutual information. Taking the reduction results as input neurons, a BP neural network with hidden layer is established in MATLAB environment. The results show that: call 85% of the track and field athletes in the sample information as training samples for training. After the simulation experiment of the model, it is found that the error value of the prediction results is basically controlled in the range of-0.025-0.05, which meets the requirements of early warning accuracy of injury risk level of athletes, and the accuracy rate of early warning of sports injury risk reaches 100%.Povzetek: Razvit je sistem za zgodnje napovedovanje športnih poškodb.

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A Systematic Mapping Study on Analysis of Code Repositories

Cited by 7 publications

References 97 publications

An Optimized Deep Learning based Technique for Grading and Extraction of Diabetic Retinopathy Severities

An Optimized Deep Learning based Technique for Grading and Extraction of Diabetic Retinopathy Severities

Aligning technical knowledge to an industry domain in global software development: a systematic mapping

Attribute Reduction Algorithm Based Early Warning Model of Sports Injury

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