This is the presentation given at chsn2020. For full article please visit the website:https://iopscience.iop.org/article/10.1088/1757-899X/1074/1/012027 or https://doi.org/10.1088/1757-899X/1074/1/012027
Agile Product Line Engineering Literature ReviewHeba Elshandidy
This is a brief presentation about the main previous work done in the area of agile product line engineering (APLE). This research area focuses on methods/frameworks/algorithms to successfully bring agile and SPLE together in order to make the best use of the advantages of each approach.
Agile Product Line Engineering Literature ReviewHeba Elshandidy
This is a brief presentation about the main previous work done in the area of agile product line engineering (APLE). This research area focuses on methods/frameworks/algorithms to successfully bring agile and SPLE together in order to make the best use of the advantages of each approach.
Class Diagram Extraction from Textual Requirements Using NLP Techniquesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Who Should Review My Code? A file-location based code-reviewer recommendation approach for modern code review.
This research study is presented at the 22nd IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER2015)
Find more information and preprint at patanamon.com
Data collection for software defect predictionAmmAr mobark
It is one of the important stages that software companies need it, it will be after produce the program and published, to know the reactions of the users and their impressions about the program and work on developing and improving it.
The Contents
* BACKGROUND AND RELATED WORK
* EXPERIMENTAL PLANNING
-Research Goal -Research Questions -Experimental Subjects
-Experimental Material -Tasks and Methods
-Experimental Design
عمار عبد الكريم صاحب مبارك
AmmAr Abdualkareem sahib mobark
Revisiting Code Ownership and Its Relationship with Software Quality in the S...The University of Adelaide
This work was presented at The 38th International Conference on Software Engineering (ICSE2016).
Abstract: Code ownership establishes a chain of responsibility for modules in large software systems. Although prior work uncovers a link between code ownership heuristics and software quality, these heuristics rely solely on the authorship of code changes. In addition to authoring code changes, developers also make important contributions to a module by reviewing code changes. Indeed, recent work shows that reviewers are highly active in modern code review processes, often suggesting alternative solutions or providing updates to the code changes. In this paper, we complement traditional code ownership heuristics using code review activity. Through a case study of six releases of the large Qt and OpenStack systems, we find that: (1) 67%-86% of developers did not author any code changes for a module, but still actively contributed by reviewing 21%-39% of the code changes, (2) code ownership heuristics that are aware of reviewing activity share a relationship with software quality, and (3) the proportion of reviewers without expertise shares a strong, increasing relationship with the likelihood of having post-release defects. Our results suggest that reviewing activity captures an important aspect of code ownership, and should be included in approximations of it in future studies.
My presentation at Ireland Oracle User Group conference, 4 April 2019.
Slideshare no longer allows uploads of new revisions, so uploaded v2 separately, with small changes around treatment of nesting ->
https://www.slideshare.net/brendanfurey7/clean-coding-in-plsql-and-sql-v2-220657522
Finding Bad Code Smells with Neural Network Models IJECEIAES
Code smell refers to any symptom introduced in design or implementation phases in the source code of a program. Such a code smell can potentially cause deeper and serious problems during software maintenance. The existing approaches to detect bad smells use detection rules or standards using a combination of different object-oriented metrics. Although a variety of software detection tools have been developed, they still have limitations and constraints in their capabilities. In this paper, a code smell detection system is presented with the neural network model that delivers the relationship between bad smells and object-oriented metrics by taking a corpus of Java projects as experimental dataset. The most well-known objectoriented metrics are considered to identify the presence of bad smells. The code smell detection system uses the twenty Java projects which are shared by many users in the GitHub repositories. The dataset of these Java projects is partitioned into mutually exclusive training and test sets. The training dataset is used to learn the network model which will predict smelly classes in this study. The optimized network model will be chosen to be evaluated on the test dataset. The experimental results show when the modelis highly trained with more dataset, the prediction outcomes are improved more and more. In addition, the accuracy of the model increases when it performs with higher epochs and many hidden layers.
A novel data type architecture support for programming languagesijpla
In programmers point of view, Datatypes in programming language level have a simple description but inside hardware, huge machine codes are responsible to describe type features. Datatype architecture design is a novel approach to match programming features along with hardware design. In this paper a novel Data type-Based Code Reducer (TYPELINE) architecture is proposed and implemented according to significant data types (SDT) of programming languages. TYPELINE uses TEUs for processing various SDT operations. This architecture design leads to reducing the number of machine codes, and increases execution speed, and also improves some parallelism level. This is because this architecture supports some operation for the execution of Abstract Data Types in parallel. Also it ensures to maintain data type features and entire application level specifications using the proposed type conversion unit. This framework includes compiler level identifying execution modes and memory management unit for decreasing object read/write in heap memory by ISA support. This energy-efficient architecture is completely compatible with object oriented programming languages and in combination mode it can process complex C++ data structures with respect to parallel TYPELINE architecture support.
8 efficient multi-document summary generation using neural networkINFOGAIN PUBLICATION
From last few years online information is growing tremendously on World Wide Web or on user’s desktops and thus online information gains much more attention in the field of automatic text summarization. Text mining has become a significant research field as it produces valuable data from unstructured and large amount of texts. Summarization systems provide the possibility of searching the important keywords of the texts and so the consumer will expend less time on reading the whole document. Main objective of summarization system is to generate a new form which expresses the key meaning of the contained text. This paper study on various existing techniques with needs of novel Multi-Document summarization schemes. This paper is motivated by arising need to provide high quality summary in very short period of time. In proposed system, user can quickly and easily access correctly-developed summaries which expresses the key meaning of the contained text. The primary focus of this paper lies with thef_β-optimal merge function, a function recently presented here, that uses the weighted harmonic mean to discover a harmony in the middle of precision and recall. Proposed system utilizes Bisect K-means clustering to improve the time and Neural Networks to improve the accuracy of summary generated by NEWSUM algorithm.
Class Diagram Extraction from Textual Requirements Using NLP Techniquesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Who Should Review My Code? A file-location based code-reviewer recommendation approach for modern code review.
This research study is presented at the 22nd IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER2015)
Find more information and preprint at patanamon.com
Data collection for software defect predictionAmmAr mobark
It is one of the important stages that software companies need it, it will be after produce the program and published, to know the reactions of the users and their impressions about the program and work on developing and improving it.
The Contents
* BACKGROUND AND RELATED WORK
* EXPERIMENTAL PLANNING
-Research Goal -Research Questions -Experimental Subjects
-Experimental Material -Tasks and Methods
-Experimental Design
عمار عبد الكريم صاحب مبارك
AmmAr Abdualkareem sahib mobark
Revisiting Code Ownership and Its Relationship with Software Quality in the S...The University of Adelaide
This work was presented at The 38th International Conference on Software Engineering (ICSE2016).
Abstract: Code ownership establishes a chain of responsibility for modules in large software systems. Although prior work uncovers a link between code ownership heuristics and software quality, these heuristics rely solely on the authorship of code changes. In addition to authoring code changes, developers also make important contributions to a module by reviewing code changes. Indeed, recent work shows that reviewers are highly active in modern code review processes, often suggesting alternative solutions or providing updates to the code changes. In this paper, we complement traditional code ownership heuristics using code review activity. Through a case study of six releases of the large Qt and OpenStack systems, we find that: (1) 67%-86% of developers did not author any code changes for a module, but still actively contributed by reviewing 21%-39% of the code changes, (2) code ownership heuristics that are aware of reviewing activity share a relationship with software quality, and (3) the proportion of reviewers without expertise shares a strong, increasing relationship with the likelihood of having post-release defects. Our results suggest that reviewing activity captures an important aspect of code ownership, and should be included in approximations of it in future studies.
My presentation at Ireland Oracle User Group conference, 4 April 2019.
Slideshare no longer allows uploads of new revisions, so uploaded v2 separately, with small changes around treatment of nesting ->
https://www.slideshare.net/brendanfurey7/clean-coding-in-plsql-and-sql-v2-220657522
Finding Bad Code Smells with Neural Network Models IJECEIAES
Code smell refers to any symptom introduced in design or implementation phases in the source code of a program. Such a code smell can potentially cause deeper and serious problems during software maintenance. The existing approaches to detect bad smells use detection rules or standards using a combination of different object-oriented metrics. Although a variety of software detection tools have been developed, they still have limitations and constraints in their capabilities. In this paper, a code smell detection system is presented with the neural network model that delivers the relationship between bad smells and object-oriented metrics by taking a corpus of Java projects as experimental dataset. The most well-known objectoriented metrics are considered to identify the presence of bad smells. The code smell detection system uses the twenty Java projects which are shared by many users in the GitHub repositories. The dataset of these Java projects is partitioned into mutually exclusive training and test sets. The training dataset is used to learn the network model which will predict smelly classes in this study. The optimized network model will be chosen to be evaluated on the test dataset. The experimental results show when the modelis highly trained with more dataset, the prediction outcomes are improved more and more. In addition, the accuracy of the model increases when it performs with higher epochs and many hidden layers.
A novel data type architecture support for programming languagesijpla
In programmers point of view, Datatypes in programming language level have a simple description but inside hardware, huge machine codes are responsible to describe type features. Datatype architecture design is a novel approach to match programming features along with hardware design. In this paper a novel Data type-Based Code Reducer (TYPELINE) architecture is proposed and implemented according to significant data types (SDT) of programming languages. TYPELINE uses TEUs for processing various SDT operations. This architecture design leads to reducing the number of machine codes, and increases execution speed, and also improves some parallelism level. This is because this architecture supports some operation for the execution of Abstract Data Types in parallel. Also it ensures to maintain data type features and entire application level specifications using the proposed type conversion unit. This framework includes compiler level identifying execution modes and memory management unit for decreasing object read/write in heap memory by ISA support. This energy-efficient architecture is completely compatible with object oriented programming languages and in combination mode it can process complex C++ data structures with respect to parallel TYPELINE architecture support.
8 efficient multi-document summary generation using neural networkINFOGAIN PUBLICATION
From last few years online information is growing tremendously on World Wide Web or on user’s desktops and thus online information gains much more attention in the field of automatic text summarization. Text mining has become a significant research field as it produces valuable data from unstructured and large amount of texts. Summarization systems provide the possibility of searching the important keywords of the texts and so the consumer will expend less time on reading the whole document. Main objective of summarization system is to generate a new form which expresses the key meaning of the contained text. This paper study on various existing techniques with needs of novel Multi-Document summarization schemes. This paper is motivated by arising need to provide high quality summary in very short period of time. In proposed system, user can quickly and easily access correctly-developed summaries which expresses the key meaning of the contained text. The primary focus of this paper lies with thef_β-optimal merge function, a function recently presented here, that uses the weighted harmonic mean to discover a harmony in the middle of precision and recall. Proposed system utilizes Bisect K-means clustering to improve the time and Neural Networks to improve the accuracy of summary generated by NEWSUM algorithm.
A new keyphrases extraction method based on suffix tree data structure for ar...ijma
Document Clustering is a branch of a larger area of scientific study known as data mining .which is an
unsupervised classification using to find a structure in a collection of unlabeled data. The useful
information in the documents can be accompanied by a large amount of noise words when using Full Text
Representation, and therefore will affect negatively the result of the clustering process. So it is with great
need to eliminate the noise words and keeping just the useful information in order to enhance the quality of
the clustering results. This problem occurs with different degree for any language such as English,
European, Hindi, Chinese, and Arabic Language. To overcome this problem, in this paper, we propose a
new and efficient Keyphrases extraction method based on the Suffix Tree data structure (KpST), the
extracted Keyphrases are then used in the clustering process instead of Full Text Representation. The
proposed method for Keyphrases extraction is language independent and therefore it may be applied to any
language. In this investigation, we are interested to deal with the Arabic language which is one of the most
complex languages. To evaluate our method, we conduct an experimental study on Arabic Documents
using the most popular Clustering approach of Hierarchical algorithms: Agglomerative Hierarchical
algorithm with seven linkage techniques and a variety of distance functions and similarity measures to
perform Arabic Document Clustering task. The obtained results show that our method for extracting
Keyphrases increases the quality of the clustering results. We propose also to study the effect of using the
stemming for the testing dataset to cluster it with the same documents clustering techniques and
similarity/distance measures.
eResearch workflows for studying free and open source software developmentAndrea Wiggins
A presentation for IFIP 2.13, Open Source Software 2008 in Milan, on the topic of using Taverna Workbench to apply eResearchh approaches for research on open source software development.
In recent years the growth of digital data is increasing dramatically, knowledge discovery and data mining have attracted immense attention with coming up need for turning such data into useful information and knowledge. Keyword extraction is considered an essential task in natural language processing (NLP) that facilitates mapping of documents to a concise set of representative single and multi-word phrases. This paper investigates using of Word2Vec and Decision Tree for keywords extraction from textual documents. The Sem-Eval (2010) dataset is used as a main input for the proposed study. The words are represented by vectors with Word2Vec technique following applying pre-processing operations on the dataset. This method is based on word similarity between candidate keywords from both collecting keywords for each label and one sample from the same label. An appropriate threshold has been determined by which the percentages that exceed this threshold are exported to the Decision Tree in order to consider an appropriate classification to be taken on the text document.
Some similarity measurements were used for the classification process. The efficiency and accuracy of the algorithm was measured in the process of classification using precision, recall and F-score rates. The obtained results indicated that using of vector representation for each keyword is an effective way to identify the most similar words, so that the opportunity to recognize the correct classification of the document increases. When using word2Vec CBOW the result of F-Score was 64% with the Gini method and WordNet Lemmatizer. Meanwhile, when using Word2Vec SG the result of F-Score was 82% with Gini Index and English Porter Stemming which considered the highest ratio for all our experiments.
http://sites.google.com/site/ijcsis/
https://google.academia.edu/JournalofComputerScience
https://www.linkedin.com/in/ijcsis-research-publications-8b916516/
http://www.researcherid.com/rid/E-1319-2016
Presentation made at the Oxford eResearch Conference 2008, describing our efforts at replicating research on open source software development and the lessons learned from using Taverna Workbench for replication.
A template based algorithm for automatic summarization and dialogue managemen...eSAT Journals
Abstract This paper describes an automated approach for extracting significant and useful events from unstructured text. The goal of research is to come out with a methodology which helps in extracting important events such as dates, places, and subjects of interest. It would be also convenient if the methodology helps in presenting the users with a shorter version of the text which contain all non-trivial information. We also discuss implementation of algorithms which exactly does this task, developed by us. Key Words: Cosine Similarity, Information, Natural Language, Summarization, Text Mining
Following are the questions which I tried to answer in this ppt
What is text summarization.
What is automatic text summarization?
How it has evolved over the time?
What are different methods?
How deep learning is used for text summarization?
business application
in first few slides extractive summarization is explained, with pro and cons in next section abstractive on is explained.
In the last section business application of each one is highlighted
Semantics-based clustering approach for similar research area detectionTELKOMNIKA JOURNAL
The manual process of searching out individuals in an already existing
research field is cumbersome and time-consuming. Prominent and rookie
researchers alike are predisposed to seek existing research publications in
a research field of interest before coming up with a thesis. From
extant literature, automated similar research area detection systems have
been developed to solve this problem. However, most of them use
keyword-matching techniques, which do not sufficiently capture the implicit
semantics of keywords thereby leaving out some research articles. In this
study, we propose the use of ontology-based pre-processing, Latent Semantic
Indexing and K-Means Clustering to develop a prototype similar research area
detection system, that can be used to determine similar research domain
publications. Our proposed system solves the challenge of high dimensionality
and data sparsity faced by the traditional document clustering technique. Our
system is evaluated with randomly selected publications from faculties
in Nigerian universities and results show that the integration of ontologies
in preprocessing provides more accurate clustering results.
Ontology Based Approach for Semantic Information Retrieval SystemIJTET Journal
Abstract—The Information retrieval system is taking an important role in current search engine which performs searching operation based on keywords which results in an enormous amount of data available to the user, from which user cannot figure out the essential and most important information. This limitation may be overcome by a new web architecture known as the semantic web which overcome the limitation of the keyword based search technique called the conceptual or the semantic search technique. Natural language processing technique is mostly implemented in a QA system for asking user’s questions and several steps are also followed for conversion of questions to the query form for retrieving an exact answer. In conceptual search, search engine interprets the meaning of the user’s query and the relation among the concepts that document contains with respect to a particular domain that produces specific answers instead of showing lists of answers. In this paper, we proposed the ontology based semantic information retrieval system and the Jena semantic web framework in which, the user enters an input query which is parsed by Standford Parser then the triplet extraction algorithm is used. For all input queries, the SPARQL query is formed and further, it is fired on the knowledge base (Ontology) which finds appropriate RDF triples in knowledge base and retrieve the relevant information using the Jena framework.
Deduplication and Author-Disambiguation of Streaming Records via Supervised M...Spark Summit
Here we present a general supervised framework for record deduplication and author-disambiguation via Spark. This work differentiates itself by – Application of Databricks and AWS makes this a scalable implementation. Compute resources are comparably lower than traditional legacy technology using big boxes 24/7. Scalability is crucial as Elsevier’s Scopus data, the biggest scientific abstract repository, covers roughly 250 million authorships from 70 million abstracts covering a few hundred years. – We create a fingerprint for each content by deep learning and/or word2vec algorithms to expedite pairwise similarity calculation. These encoders substantially reduce compute time while maintaining semantic similarity (unlike traditional TFIDF or predefined taxonomies). We will briefly discuss how to optimize word2vec training with high parallelization. Moreover, we show how these encoders can be used to derive a standard representation for all our entities namely such as documents, authors, users, journals, etc. This standard representation can simplify the recommendation problem into a pairwise similarity search and hence it can offer a basic recommender for cross-product applications where we may not have a dedicate recommender engine designed. – Traditional author-disambiguation or record deduplication algorithms are batch-processing with small to no training data. However, we have roughly 25 million authorships that are manually curated or corrected upon user feedback. Hence, it is crucial to maintain historical profiles and hence we have developed a machine learning implementation to deal with data streams and process them in mini batches or one document at a time. We will discuss how to measure the accuracy of such a system, how to tune it and how to process the raw data of pairwise similarity function into final clusters. Lessons learned from this talk can help all sort of companies where they want to integrate their data or deduplicate their user/customer/product databases.
Similar to Keyphrase Extraction And Source Code Similarity Detection- A Survey (20)
Possibility of interdisciplinary research software engineering andNakul Sharma
This Presentation is a summary of the paper published at Advances in Information Science and Service Sciences (AISS), South Korea. The presentation tries to highlight the essence of the paper.
Integrating natural language processing and software engineeringNakul Sharma
This presentation is the brief summary of the paper published at International Journal in Software Engineering and Its Applications, South Korea, Nov-2015.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Keyphrase Extraction And Source Code Similarity Detection- A Survey
1. Keyphrase Extraction And Source
Code Similarity Detection- A Survey
Paper ID 123
Presented By
Nakul Sharma,Dr. Prasanth Yalla
Department of Computer Science and Engineering
Koneru Lakshmaiah Education Foundation
Vaddeswaram,Guntur-522502, India.
2. Agenda
• Abstract
• Introduction
• Literature Survey on Keyphrase Extraction & Source
Code Similarity Calculation
– LR on survey papers
– LR on Keyphrase Extraction
– LR on Source Code Similarity Calculation
• Evaluation of System Developed
• Recommendation System Keyphrase V/S Source Code
based
• Conclusion
3. Abstract
• Keyphrase extraction is the starting phase of feature extraction and
many other NLP tasks.
• Hence, keyphrase extraction ensures separation of essential and
relevant information from rest of the document or corpus.
• Similarity score is used for detecting similarity between two or
more documents. In this paper, a summary of existing research
conducted in the field of keyphrase extraction and source code
similarity detection is presented.
• The literature focuses on more concept based research instead of
going towards domain level. There exist a promising potential for
creation of recommendation systems by employing similarly
situated research techniques.
4. Introduction
• Keyphrase Extraction (KE) involves getting the
essential and relevant words from a given
document or a text.
• Used in text mining and its allied fields.
• Source Code Analysis (SCA) forms the basis for
program understanding and comprehension.
• Source Code Analysis can also aid in
development of recommendation system for
the developer all its allied roles.
7. LR on survey papers
• An Overview of Graph-Based Keyword Extraction
Methods and Approaches
– The authors, in this work, provide an introduction to
Keyphrase Extraction basics. The authors provide a
classification of KE methods research. This is followed by
the categorization of keyphrase extraction techniques. The
paper provides a strong mathematical base for graph-
based keyword extraction research.
• Automatic keyphrase extraction: a survey and trends”
– The paper by Zakariae et. al. provides a major review on
the current existing state-of-art research on Automatic
Keyphrase Extraction (APKE).
8. Literature Review Of Existing Work On
Keyphrase Extraction
Title of Paper Advantage of Proposed Work Disadvantages of Proposed Work
A New Multi-lingual
Knowledge-base
Approach to Keyphrase
Extraction for the Italian
Language
Authors propose creating a multi-lingual
tool for extracting keyphrases of English
and Italian language
Authors did not consider keyphrase extraction for
different languages.
Keyphrase generation can be extended for other
languages also.
Knowledge-Based
Techniques for Scholarly
Data Access: Towards
Automatic Curation
The authors propose a system that is
useful for researchers by using data
mining, NLP, clustering, Graph-based
approaches.
This is a major project & needs multiple domain level
experts for its proper execution
A Distributed
Framework for NLP-
Based Keyword and
Keyphrase Extraction
From Web Pages and
Documents
The authors use keyphrase-extraction in
cloud based systems. GATE
architecture is employed on cloud. The
web crawler is also integrated into the
system
The more testing of GATE based applications can be
done. Hadoop implementation can also be extended
9. Title of Paper Advantage of Proposed Work Disadvantages of Proposed Work
A New Multi-lingual
Knowledge-base
Approach to Keyphrase
Extraction for the Italian
Language
Authors propose creating a multi-lingual
tool for extracting keyphrases of English
and Italian language
Authors did not consider keyphrase extraction for
different languages.
Keyphrase generation can be extended for other
languages also.
Knowledge-Based
Techniques for Scholarly
Data Access: Towards
Automatic Curation
The authors propose a system that is
useful for researchers by using data
mining, NLP, clustering, Graph-based
approaches.
This is a major project & needs multiple domain level
experts for its proper execution
A Distributed
Framework for NLP-
Based Keyword and
Keyphrase Extraction
From Web Pages and
Documents
The authors use keyphrase-extraction in
cloud based systems. GATE
architecture is employed on cloud. The
web crawler is also integrated into the
system
The more testing of GATE based applications can be
done. Hadoop implemetation can also be extended
Building a Construction
Project Key-Phrase
Network from
Unstructured Text
Documents
For unstructured text, authors propose
document management system, project
keyphrase network, support libraries &
system for civil engineering projects
The current work can be extended for contextual
awareness, interactive interface and controlled
vocabulary
Identifying Candidate
Tasks for Robotic
Process
Automation in Textual
Process Descriptions
Authors identify& classify tasks using
BPMN for textual processes
In practice textual descriptions are not the same as
provided in the paper.
10. Title of Paper Advantage of Proposed Work Disadvantages of Proposed Work
Simple Unsupervised
Keyphrase Extraction
using Sentence
Embeddings
Authors make use of EmbedRank, to
extract keyphrases from single document.
The unsupervised ML algorithm is used
for the process. Sentence embedding is
employed in the process.
Unsupervised approach may introduce biasing due to
inherit noise in the document.
How Document Pre-
processing affects
Keyphrase Extraction
Performance
Authors reassess the performance of five
keyphrase extraction models and
conclude that document pre-processing
affects performance.
More keyphrase extraction models such as
ExpandRank, SingleRank can be used for assessment.
An Unsupervised
Approach for Keyphrase
Extraction Using Within-
Collection Resources
Authors use graph-based ranking for
checking relevance across the words. The
topic-based cluster for the purpose of
semantic enrichment.
The work is restricted to only scholarly documents
Keyphrase Extraction
Based on Prior
Knowledge
Authors make use of controlled
vocabulary, prior-probability & supervised
ML for Keyphrase Extraction
None mentioned
11. Title of Paper Advantage of Proposed Work Disadvantages of Proposed Work
21
A graph-based
unsupervised N-gram
filtration technique
for automatic
keyphrase extraction
Authors used ngram approach for
candidate selection and ranking of
candidate keyphrases.
As resources become multilingual, it is possible
to extend the authors work.
22
Feature selection,
optimization and
clustering strategies
of text documents
Authors provided a review of
clustering and its allied research
areas including similarity measures.
Authors conclude text clustering
research focussed on dynamic &
Heterogeneous clustering
applications
Community-driven clustering could not be
addressed in the given literature.
23
Keyphrase extraction
as sequence labeling
using contextualized
embeddings
Authors make use of BiLSTM-CRF
ML technique architecture for
keyphrase extraction and including
embedding the context.
The authors work can be extended to keyphrase
generation
24
DAKE: Document-
Level Attention for
Keyphrase Extraction
Authors makes use bi-directional
LSTM which extracts keyphrases
from documents.
The source documents are not multilingual
12. Literature Review of Research Papers
Related to Source Code Analysis
Based Similarity Index
Title of Paper Advantage Research of Paper Disadvantage/Future Scope of Research
Paper
Building Program Vector
Representations for Deep
Learning
Author applied vector representations to code
analysis.
DL can be applied to complete domain of SCA.
TF-IDF Inspired Detection
for Cross Language Source
Code Plagiarism and
Collusion
The author applied vector representations to code
analysis. DL can be applied to complete domain
of SCA
Identifier renaming disguise to be done based
on first occurrence.
A Scalable Code Similarity
Detection with Online
Architecture and Focused
Comparison for Maintaining
Academic Integrity in
Programming
Cosine similarity introduced by the authors
reduces lead to short running time especially with
large token strings.
The techniques proposed can be proposed for
some other programming courses as well.
13. Evaluation of System Developed
• Keyphrase Extraction based
– Preference measure (bpref)
– Mean Reciprocal Rank (MRR)
– Precision
– Recall
– F-measure
14. Recommendation System Keyphrase
V/S Source Code based
Keyphrase-Based RS Source Code-Based RS
Part of a Larger System including Source Code-
based RS
Specific to Source Code
Addressed for larger audience Addressed for specific audience
Domain-level concepts can be addressed Domain-level concepts can be addressed
15. Conclusion
• In the current work, an effort is made to
summarize the existing state-of-art work in
the field of Keyphrase Extraction and similarity
measure based source code analysis.
• Keyphrase extraction is more related to the
pre-processing stage of Source Code Analysis.
Similarity measures denote a statistical term
of measurement of any file based parameter.
16. References
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Udine, Italy, 2016.
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17. Cite This Article As
• Nakul Sharma and Prasanth Yalla 2021 IOP
Conf. Ser.: Mater. Sci. Eng. 1074 012027,
https://iopscience.iop.org/article/10.1088/17
57-899X/1074/1/012027
• Complete article available at :
– https://iopscience.iop.org/article/10.1088/1757-
899X/1074/1/012027