This document summarizes research on automatically classifying and extracting non-functional requirements (NFRs) from text files using supervised machine learning. The researchers created a dataset of NFR keywords by analyzing NFR catalogs identified through a systematic mapping study. The keywords were categorized into security, performance, and usability. They then tested a supervised learning approach on a existing dataset containing 625 software specifications. The approach achieved accuracy rates between 85-98% for classifying NFRs into the security, performance, and usability categories. The research thus provides a way to generate labeled datasets for training machine learning models to automatically classify NFRs mentioned in text documents.
LEAN THINKING IN SOFTWARE ENGINEERING: A SYSTEMATIC REVIEWijseajournal
The field of Software Engineering has suffered considerable transformation in the last decades due to the influence of the philosophy of Lean Thinking. The purpose of this systematic review is to identify practices and approaches proposed by researchers in this area in the last 5 years, who have worked under the influence of this thinking. The search strategy brought together 549 studies, 80 of which were classified as
relevant for synthesis in this review. Seventeen tools of Lean Thinking adapted to Software Engineering were catalogued, as well as 35 practices created for the development of software that has been influenced by this philosophy. The study rovides a roadmap of results with the current state of the art and the identification of gaps pointing to opportunities for further esearch.
A Review Of Code Reviewer Recommendation Studies Challenges And Future Direc...Sheila Sinclair
This document summarizes a systematic literature review of 29 studies on code reviewer recommendation published between 2009 and 2020. The review aims to categorize the methods, datasets, evaluation approaches, reproducibility, validity threats, and future directions discussed in the studies. Most studies used heuristic or machine learning approaches, evaluated models on open source projects, and discussed refining models and additional experiments as future work. Common challenges included ensuring reproducibility and dealing with threats to model generalizability and dataset integrity.
This document presents a study on the theoretical and empirical validation that has been done on aspect-oriented software maintainability metrics. It describes the methodology used, which involved searching literature sources and selecting papers related to aspect-oriented maintainability metrics. The results are discussed in tables, showing that most papers focus on empirical validation of metrics rather than theoretical validation. Several papers are described that empirically validated specific metrics related to maintainability. However, the study notes that more theoretical validation of metrics is still needed before empirical validation. Threats to validity, such as bias and limited data extraction, are also presented.
This document discusses an efficient tool for a reusable software component taxonomy. It proposes integrating two existing classification schemes to develop a prototype system. Specifically:
- It proposes developing an integrated classification scheme using a combination of existing schemes to better classify and store reusable software components in a repository for efficient retrieval.
- A prototype was developed that integrates two existing classification schemes to demonstrate the proposed approach. This aims to improve on limitations of current software component retrieval methods.
Knowledge Graph and Similarity Based Retrieval Method for Query Answering SystemIRJET Journal
This document proposes a knowledge graph and question answering system to extract and analyze information from large volumes of unstructured data like annual reports. It discusses using natural language processing techniques like named entity recognition with spaCy and dependency parsing to extract entity-relation pairs from text and construct a knowledge graph. For question answering, it analyzes user queries with similar NLP approaches and then matches query triplets to the knowledge graph to retrieve answers, combining information retrieval and trained classifiers. The proposed system aims to provide faster understanding and analysis of complex, unstructured data for professionals.
Vertical intent prediction approach based on Doc2vec and convolutional neural...IJECEIAES
Vertical selection is the task of selecting the most relevant verticals to a given query in order to improve the diversity and quality of web search results. This task requires not only predicting relevant verticals but also these verticals must be those the user expects to be relevant for his particular information need. Most existing works focused on using traditional machine learning techniques to combine multiple types of features for selecting several relevant verticals. Although these techniques are very efficient, handling vertical selection with high accuracy is still a challenging research task. In this paper, we propose an approach for improving vertical selection in order to satisfy the user vertical intent and reduce user’s browsing time and efforts. First, it generates query embeddings vectors using the doc2vec algorithm that preserves syntactic and semantic information within each query. Secondly, this vector will be used as input to a convolutional neural network model for increasing the representation of the query with multiple levels of abstraction including rich semantic information and then creating a global summarization of the query features. We demonstrate the effectiveness of our approach through comprehensive experimentation using various datasets. Our experimental findings show that our system achieves significant accuracy. Further, it realizes accurate predictions on new unseen data.
This document summarizes a research paper on developing a feature-based product recommendation system. It begins by introducing recommender systems and their importance for e-commerce. It then describes how the proposed system takes basic product descriptions as input, recognizes features using association rule mining and k-nearest neighbor algorithms, and outputs recommended additional features to improve the product profile. The paper evaluates the system's performance on recommending antivirus software features. In under 3 sentences.
A Systematic Mapping Review of Software Quality Measurement: Research Trends,...IJECEIAES
Software quality is a key for the success in the business of information and technology. Hence, before be marketed, it needs the software quality measurement to fulfill the user requirements. Some methods of the software quality analysis have been tested in a different perspective, and we have presented the software method in the point of view of users and experts. This study aims to map the method of software quality measurement in any models of quality. Using the method of Systematic Mapping Study, we did a searching and filtering of papers using the inclusion and exclusion criteria. 42 relevant papers have been obtained then. The result of the mapping showed that though the model of ISO SQuaRE has been widely used since the last five years and experienced the dynamics, the researchers in Indonesia still used ISO9126 until the end of 2016.The most commonly used method of the software quality measurement Method is the empirical method, and some researchers have done an AHP and Fuzzy approach in measuring the software quality.
LEAN THINKING IN SOFTWARE ENGINEERING: A SYSTEMATIC REVIEWijseajournal
The field of Software Engineering has suffered considerable transformation in the last decades due to the influence of the philosophy of Lean Thinking. The purpose of this systematic review is to identify practices and approaches proposed by researchers in this area in the last 5 years, who have worked under the influence of this thinking. The search strategy brought together 549 studies, 80 of which were classified as
relevant for synthesis in this review. Seventeen tools of Lean Thinking adapted to Software Engineering were catalogued, as well as 35 practices created for the development of software that has been influenced by this philosophy. The study rovides a roadmap of results with the current state of the art and the identification of gaps pointing to opportunities for further esearch.
A Review Of Code Reviewer Recommendation Studies Challenges And Future Direc...Sheila Sinclair
This document summarizes a systematic literature review of 29 studies on code reviewer recommendation published between 2009 and 2020. The review aims to categorize the methods, datasets, evaluation approaches, reproducibility, validity threats, and future directions discussed in the studies. Most studies used heuristic or machine learning approaches, evaluated models on open source projects, and discussed refining models and additional experiments as future work. Common challenges included ensuring reproducibility and dealing with threats to model generalizability and dataset integrity.
This document presents a study on the theoretical and empirical validation that has been done on aspect-oriented software maintainability metrics. It describes the methodology used, which involved searching literature sources and selecting papers related to aspect-oriented maintainability metrics. The results are discussed in tables, showing that most papers focus on empirical validation of metrics rather than theoretical validation. Several papers are described that empirically validated specific metrics related to maintainability. However, the study notes that more theoretical validation of metrics is still needed before empirical validation. Threats to validity, such as bias and limited data extraction, are also presented.
This document discusses an efficient tool for a reusable software component taxonomy. It proposes integrating two existing classification schemes to develop a prototype system. Specifically:
- It proposes developing an integrated classification scheme using a combination of existing schemes to better classify and store reusable software components in a repository for efficient retrieval.
- A prototype was developed that integrates two existing classification schemes to demonstrate the proposed approach. This aims to improve on limitations of current software component retrieval methods.
Knowledge Graph and Similarity Based Retrieval Method for Query Answering SystemIRJET Journal
This document proposes a knowledge graph and question answering system to extract and analyze information from large volumes of unstructured data like annual reports. It discusses using natural language processing techniques like named entity recognition with spaCy and dependency parsing to extract entity-relation pairs from text and construct a knowledge graph. For question answering, it analyzes user queries with similar NLP approaches and then matches query triplets to the knowledge graph to retrieve answers, combining information retrieval and trained classifiers. The proposed system aims to provide faster understanding and analysis of complex, unstructured data for professionals.
Vertical intent prediction approach based on Doc2vec and convolutional neural...IJECEIAES
Vertical selection is the task of selecting the most relevant verticals to a given query in order to improve the diversity and quality of web search results. This task requires not only predicting relevant verticals but also these verticals must be those the user expects to be relevant for his particular information need. Most existing works focused on using traditional machine learning techniques to combine multiple types of features for selecting several relevant verticals. Although these techniques are very efficient, handling vertical selection with high accuracy is still a challenging research task. In this paper, we propose an approach for improving vertical selection in order to satisfy the user vertical intent and reduce user’s browsing time and efforts. First, it generates query embeddings vectors using the doc2vec algorithm that preserves syntactic and semantic information within each query. Secondly, this vector will be used as input to a convolutional neural network model for increasing the representation of the query with multiple levels of abstraction including rich semantic information and then creating a global summarization of the query features. We demonstrate the effectiveness of our approach through comprehensive experimentation using various datasets. Our experimental findings show that our system achieves significant accuracy. Further, it realizes accurate predictions on new unseen data.
This document summarizes a research paper on developing a feature-based product recommendation system. It begins by introducing recommender systems and their importance for e-commerce. It then describes how the proposed system takes basic product descriptions as input, recognizes features using association rule mining and k-nearest neighbor algorithms, and outputs recommended additional features to improve the product profile. The paper evaluates the system's performance on recommending antivirus software features. In under 3 sentences.
A Systematic Mapping Review of Software Quality Measurement: Research Trends,...IJECEIAES
Software quality is a key for the success in the business of information and technology. Hence, before be marketed, it needs the software quality measurement to fulfill the user requirements. Some methods of the software quality analysis have been tested in a different perspective, and we have presented the software method in the point of view of users and experts. This study aims to map the method of software quality measurement in any models of quality. Using the method of Systematic Mapping Study, we did a searching and filtering of papers using the inclusion and exclusion criteria. 42 relevant papers have been obtained then. The result of the mapping showed that though the model of ISO SQuaRE has been widely used since the last five years and experienced the dynamics, the researchers in Indonesia still used ISO9126 until the end of 2016.The most commonly used method of the software quality measurement Method is the empirical method, and some researchers have done an AHP and Fuzzy approach in measuring the software quality.
Text preprocessing and document classification plays a vital role in web services discovery. Nearest centroid classifiers were mostly employed in high-dimensional application including genomics. Feature selection is a major problem in all classifiers and in this paper we propose to use an effective feature selection procedure followed by web services discovery through Centroid classifier algorithm. The task here in this problem statement is to effectively assign a document to one or more classes. Besides being simple and robust, the centroid classifier s not effectively used for document classification due to the computational complexity and larger memory requirements. We address these problems through dimensionality reduction and effective feature set selection before training and testing the classifier. Our preliminary experimentation and results shows that the proposed method outperforms other algorithms mentioned in the literature including K-Nearest neighbors, Naive Bayes classifier and Support Vector Machines.
IRJET- Automated Document Summarization and Classification using Deep Lear...IRJET Journal
The document proposes a system that uses deep learning methods for automated document summarization and classification. It uses a recurrent convolutional neural network (RCNN) which combines a convolutional neural network and recurrent neural network to build a robust classifier model. For summarization, it employs a graph-based method inspired by PageRank to extract the top 20% of sentences from a document based on word intersections. The RCNN model achieved over 97% accuracy on classifying documents from various domains using their summaries. The system aims to speed up classification and make it more intuitive using automated summarization techniques with deep learning.
1 Abstract—Autonomous Vehicles (AV) are expected to.docxShiraPrater50
1
Abstract—Autonomous Vehicles (AV) are expected to bring
considerable benefits to society, such as traffic optimization and
accidents reduction. They rely heavily on advances in many
Artificial Intelligence (AI) approaches and techniques. However,
while some researchers in this field believe AI is the core element
to enhance safety, others believe AI imposes new challenges to
assure the safety of these new AI-based systems and applications.
In this non-convergent context, this paper presents a systematic
literature review to paint a clear picture of the state of the art of
the literature in AI on AV safety. Based on an initial sample of
4870 retrieved papers, 59 studies were selected as the result of the
selection criteria detailed in the paper. The shortlisted studies were
then mapped into six categories to answer the proposed research
questions. An AV system model was proposed and applied to
orient the discussions about the SLR findings. As a main result, we
have reinforced our preliminary observation about the necessity
of considering a serious safety agenda for the future studies on AI-
based AV systems.
Keywords: Autonomous vehicles, safety, artificial intelligence,
machine intelligence, machine learning, SLR.
I. INTRODUCTION
Advances in Artificial Intelligence (AI) are one of the key
enablers of the Autonomous Vehicles (AVs) development. In
fact, AVs rely on AI to interpret the environment, understand
its conditions, and make driving-related decisions. Thus, it
basically replicates the human driver actions when driving a
vehicle. In this context, AI applied to AV has become an
important research topic.
AV is a safety-critical system. When operating in an
undesirable way, AV can jeopardize human lives or the
environment in which it operates. It has the potential to threaten
the lives of its own passengers, pedestrians and people in other
vehicles, and damage other transportation system elements (e.g.
other vehicles and transportation infrastructure). Therefore, it is
mandatory to assure AV is safe, mainly when operating on
public roads in which resources will be shared with other
systems (and people).
Although safety is a mandatory characteristic to AV, and
although the researchers seem to agree on the importance of AI
This work was supported in part by the Research, Development and
innovation Center, Ericsson Telecommunication S.A., Brazil.
1Safety Analysis Group from Polytechnic School of University of São Paulo,
São Paulo, Brazil (e-mail: [email protected]).
applied to autonomous vehicles, they seem to disagree on the
AIs impact on AV safety. Many researchers, in special those
related to the AI community and AV manufacturers, advocate
AI as one of the core elements to enhance AV safety. Their
hypothesis is the automation of the driving tasks will lead to a
significant reduction of the car accidents. However, other
researchers, ...
A Survey on Design Pattern Detection ApproachesCSCJournals
Design patterns play a key role in software development process. The interest in extracting design pattern instances from object-oriented software has increased tremendously in the last two decades. Design patterns enhance program understanding, help to document the systems and capture design trade-offs.
This paper provides the current state of the art in design patterns detection. The selected approaches cover the whole spectrum of the research in design patterns detection. We noticed diverse accuracy values extracted by different detection approaches. The lessons learned are listed at the end of this paper, which can be used for future research directions and guidelines in the area of design patterns detection.
ANALYZABILITY METRIC FOR MAINTAINABILITY OF OBJECT ORIENTED SOFTWARE SYSTEMIAEME Publication
Analyzability is one of the major factors in the prediction of maintainability aspect that can improve the quality of the intended software solution in an appropriate manner. In fact, the right analyzability measure will go a long way in decreasing the inadequacies and deficiencies through identification of the modified parts and failure causes in the software system. The analyzability metric identification is not possible in every software solution because of its non- functional nature in the real world, but in object-oriented software system there is an opportunity to find out the analyzability metric in the form of class inheritance hierarchies. In this research paper, the researcher measured the analyzability factor for the object-oriented software systems and also validated in accordance with the famed weyker’s properties. The proposed analyzability metric here is intended to lead the new developments in object-oriented software maintainability parameter in future and help the new researchers do their research the right way thus eventually achieving the quality aspect of the objectoriented software system and fulfilling the needs of the users.
This work aims to provide a practical guide to assist students of Computer Science
courses and related fields to conduct a systematic literature review. The steps proposed
in this paper to conduct a systematic review were extracted from a technical report
published by the researcher Bárbara Kitchenham [1] and arranged in a more objective
format
IRJET - Student Pass Percentage Dedection using Ensemble LearninngIRJET Journal
This document discusses using ensemble learning methods to predict student pass rates. It begins with an abstract describing ensemble learning and its applications. It then provides background on strengthening the STEM workforce and using prediction modeling in educational data mining. The methodology section describes using decision trees, logistic regression, nearest neighbors, neural networks, naive Bayes, and support vector machines as base classifiers in an ensemble model to predict student enrollment in STEM courses. The results show the J48 decision tree algorithm correctly classified 84% of instances, outperforming naive Bayes and CART. The conclusion is that ensemble models can better categorize factors affecting student choice to enroll in STEM by combining multiple classification techniques.
A Novel approach for Document Clustering using Concept ExtractionAM Publications
In this paper we present a novel approach to extract the concept from a document and cluster such set of documents depending on the concept extracted from each of them. We transform the corpus into vector space by using term frequency–inverse document frequency then calculate the cosine distance between each document, followed by clustering them using K means algorithm. We also use multidimensional scaling to reduce the dimensionality within the corpus. It results in the grouping of documents which are most similar to each other with respect to their content and the genre.
Maturing software engineering knowledge through classificationsimmortalchhetri
The document provides an overview of research conducted on classifying unit testing techniques. It discusses previous work on testing technique classifications and identifies their limitations. The research aims to develop a new classification scheme using operational and historical criteria. 30 classification criteria are identified and used to analyze 13 testing techniques. The results show the new classification improves efficiency and usability when selecting techniques compared to previous schemes. Knowledge gaps in existing techniques are also examined.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
An in-depth review on News Classification through NLPIRJET Journal
This document provides an in-depth literature review of news classification through natural language processing (NLP). It discusses several existing approaches to news classification, including models that use convolutional neural networks (CNNs), graph-based approaches, and attention mechanisms. The document also notes that current search engines often return too many irrelevant results, so classification could help layer search results. It concludes that while many techniques have been developed, inconsistencies remain in effectively classifying news, so further research on combining NLP, feature extraction, and fuzzy logic is needed.
Development of Information Extraction for Data Analysis using NLPIRJET Journal
This document describes a proposed algorithm for extracting information from PDF documents using natural language processing (NLP). The algorithm aims to automate the extraction of key data like company metrics and financial details that analysts currently extract manually. It involves identifying keywords, extracting text and tables using rule-based filters, and presenting the extracted information in a structured format like a table. The algorithm is intended to simplify the information extraction process and make it scalable for large documents. It provides a framework that can be modified based on user needs and categories of interest.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IRJET- Determining Document Relevance using Keyword ExtractionIRJET Journal
This document describes a system that aims to search for and retrieve relevant documents from a large collection based on a user's query. It does this through three main components: keyword extraction, document searching, and a question answering bot. Keyword extraction is done using the TF-IDF algorithm to identify important words in documents. These keywords are stored in a database along with their TF-IDF weights. When a user submits a query, the system searches for documents containing keywords from the query and returns relevant results. It also includes a feedback mechanism for users to improve search accuracy over time. The goal is to deliver accurate search results quickly from large document collections.
STATE OF THE ART SURVEY ON DSPL SECURITY CHALLENGESIJCSES Journal
The Dynamic Software Product Line (DSPL) is becoming the system with high vulnerability and high confidentiality in which the adaptive security is a challenging task and critical for it to operate. Adaptive security is able to automatically select security mechanisms and their parameters at runtime in order to preserve the required security level in a changing environment. This paper presents a literature review of
security adaptation approaches for DSPL, and evaluates them in terms of how well they support critical
security services and what level of adaptation they achieve. This work will be done following the Systematic
Review approach. Our results concluded that the research field of security approaches for DSPL is still
poor of methods and metrics for evaluating and comparing different techniques. The comparison reveals
that the existing adaptive security approaches widely cover the information gathering. However, comparative approaches do not describe how to decide on a method for performing adaptive security DSPL or how to provide knowledge input for adapting security. Therefore, these areas of research are promising.
TOWARDS CROSS-BROWSER INCOMPATIBILITIES DETECTION: ASYSTEMATIC LITERATURE REVIEWijseajournal
The term Cross Browser Incompatibilities (XBI) stands for compatibility issues which can be observed while rendering a web application in different browsers, such as: Microsoft Edge, Mozilla Firefox, Google Chrome, among others. In order to detect Cross-Browser Incompatibilities - XBIs, Web developers rely on manual inspection of every web page of their applications, rendering them in various configuration environments (considering multiple OS platforms and browser versions). These manual inspections are error-prone and increase the cost of the Web engineering process. This paper has the goal of identify- ing techniques for automatic XBI detection, conducting a Systematic Literature Review (SLR) with focus on studies which report XBI automatic detection strategies. The selected studies were compared accord- ing to the different technological solutions they implemented and were used to elaborate a XBI detection framework which can organize and classify the state-of-the-art techniques and may be used to guide future research activities about this topic.
An Empirical Study of the Improved SPLD Framework using Expert Opinion TechniqueIJEACS
Due to the growing need for high-performance and low-cost software applications and the increasing competitiveness, the industry is under pressure to deliver products with low development cost, reduced delivery time and improved quality. To address these demands, researchers have proposed several development methodologies and frameworks. One of the latest methodologies is software product line (SPL) which utilizes the concepts like reusability and variability to deliver successful products with shorter time-to-market, least development and minimum maintenance cost with a high-quality product. This research paper is a validation of our proposed framework, Improved Software Product Line (ISPL), using Expert Opinion Technique. An extensive survey based on a set of questionnaires on various aspects and sub-processes of the ISPLD Framework was carried. Analysis of the empirical data concludes that ISPL shows significant improvements on several aspects of the contemporary SPL frameworks.
On Using Network Science in Mining Developers Collaboration in Software Engin...IJDKP
Background: Network science is the set of mathematical frameworks, models, and measures that are used to understand a complex system modeled as a network composed of nodes and edges. The nodes of a network represent entities and the edges represent relationships between these entities. Network science has been used in many research works for mining human interaction during different phases of software engineering (SE). Objective: The goal of this study is to identify, review, and analyze the published research works that used network analysis as a tool for understanding the human collaboration on different levels of software development. This study and its findings are expected to be of benefit for software engineering practitioners and researchers who are mining software repositories using tools from network science field. Method: We conducted a systematic literature review, in which we analyzed a number of selected papers from different digital libraries based on inclusion and exclusion criteria. Results: We identified 35 primary studies (PSs) from four digital libraries, then we extracted data from each PS according to a predefined data extraction sheet. The results of our data analysis showed that not all of the constructed networks used in the PSs were valid as the edges of these networks did not reflect a real relationship between the entities of the network. Additionally, the used measures in the PSs were in many cases not suitable for the used networks. Also, the reported analysis results by the PSs were not, in most cases, validated using any statistical model. Finally, many of the PSs did not provide lessons or guidelines for software practitioners that can improve the software engineering practices. Conclusion: Although employing network analysis in mining developers’ collaboration showed some satisfactory results in some of the PSs, the application of network analysis needs to be conducted more carefully. That is said, the constructed network should be representative and meaningful, the used measure needs to be suitable for the context, and the validation of the results should be considered. More and above, we state some research gaps, in which network science can be applied, with some pointers to recent advances that can be used to mine collaboration networks.
On Using Network Science in Mining Developers Collaboration in Software Engin...IJDKP
Background: Network science is the set of mathematical frameworks, models, and measures that are
used to understand a complex system modeled as a network composed of nodes and edges. The nodes of a network
represent entities and the edges represent relationships between these entities. Network science has been used in
many research works for mining human interaction during different phases of software engineering (SE)
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Text preprocessing and document classification plays a vital role in web services discovery. Nearest centroid classifiers were mostly employed in high-dimensional application including genomics. Feature selection is a major problem in all classifiers and in this paper we propose to use an effective feature selection procedure followed by web services discovery through Centroid classifier algorithm. The task here in this problem statement is to effectively assign a document to one or more classes. Besides being simple and robust, the centroid classifier s not effectively used for document classification due to the computational complexity and larger memory requirements. We address these problems through dimensionality reduction and effective feature set selection before training and testing the classifier. Our preliminary experimentation and results shows that the proposed method outperforms other algorithms mentioned in the literature including K-Nearest neighbors, Naive Bayes classifier and Support Vector Machines.
IRJET- Automated Document Summarization and Classification using Deep Lear...IRJET Journal
The document proposes a system that uses deep learning methods for automated document summarization and classification. It uses a recurrent convolutional neural network (RCNN) which combines a convolutional neural network and recurrent neural network to build a robust classifier model. For summarization, it employs a graph-based method inspired by PageRank to extract the top 20% of sentences from a document based on word intersections. The RCNN model achieved over 97% accuracy on classifying documents from various domains using their summaries. The system aims to speed up classification and make it more intuitive using automated summarization techniques with deep learning.
1 Abstract—Autonomous Vehicles (AV) are expected to.docxShiraPrater50
1
Abstract—Autonomous Vehicles (AV) are expected to bring
considerable benefits to society, such as traffic optimization and
accidents reduction. They rely heavily on advances in many
Artificial Intelligence (AI) approaches and techniques. However,
while some researchers in this field believe AI is the core element
to enhance safety, others believe AI imposes new challenges to
assure the safety of these new AI-based systems and applications.
In this non-convergent context, this paper presents a systematic
literature review to paint a clear picture of the state of the art of
the literature in AI on AV safety. Based on an initial sample of
4870 retrieved papers, 59 studies were selected as the result of the
selection criteria detailed in the paper. The shortlisted studies were
then mapped into six categories to answer the proposed research
questions. An AV system model was proposed and applied to
orient the discussions about the SLR findings. As a main result, we
have reinforced our preliminary observation about the necessity
of considering a serious safety agenda for the future studies on AI-
based AV systems.
Keywords: Autonomous vehicles, safety, artificial intelligence,
machine intelligence, machine learning, SLR.
I. INTRODUCTION
Advances in Artificial Intelligence (AI) are one of the key
enablers of the Autonomous Vehicles (AVs) development. In
fact, AVs rely on AI to interpret the environment, understand
its conditions, and make driving-related decisions. Thus, it
basically replicates the human driver actions when driving a
vehicle. In this context, AI applied to AV has become an
important research topic.
AV is a safety-critical system. When operating in an
undesirable way, AV can jeopardize human lives or the
environment in which it operates. It has the potential to threaten
the lives of its own passengers, pedestrians and people in other
vehicles, and damage other transportation system elements (e.g.
other vehicles and transportation infrastructure). Therefore, it is
mandatory to assure AV is safe, mainly when operating on
public roads in which resources will be shared with other
systems (and people).
Although safety is a mandatory characteristic to AV, and
although the researchers seem to agree on the importance of AI
This work was supported in part by the Research, Development and
innovation Center, Ericsson Telecommunication S.A., Brazil.
1Safety Analysis Group from Polytechnic School of University of São Paulo,
São Paulo, Brazil (e-mail: [email protected]).
applied to autonomous vehicles, they seem to disagree on the
AIs impact on AV safety. Many researchers, in special those
related to the AI community and AV manufacturers, advocate
AI as one of the core elements to enhance AV safety. Their
hypothesis is the automation of the driving tasks will lead to a
significant reduction of the car accidents. However, other
researchers, ...
A Survey on Design Pattern Detection ApproachesCSCJournals
Design patterns play a key role in software development process. The interest in extracting design pattern instances from object-oriented software has increased tremendously in the last two decades. Design patterns enhance program understanding, help to document the systems and capture design trade-offs.
This paper provides the current state of the art in design patterns detection. The selected approaches cover the whole spectrum of the research in design patterns detection. We noticed diverse accuracy values extracted by different detection approaches. The lessons learned are listed at the end of this paper, which can be used for future research directions and guidelines in the area of design patterns detection.
ANALYZABILITY METRIC FOR MAINTAINABILITY OF OBJECT ORIENTED SOFTWARE SYSTEMIAEME Publication
Analyzability is one of the major factors in the prediction of maintainability aspect that can improve the quality of the intended software solution in an appropriate manner. In fact, the right analyzability measure will go a long way in decreasing the inadequacies and deficiencies through identification of the modified parts and failure causes in the software system. The analyzability metric identification is not possible in every software solution because of its non- functional nature in the real world, but in object-oriented software system there is an opportunity to find out the analyzability metric in the form of class inheritance hierarchies. In this research paper, the researcher measured the analyzability factor for the object-oriented software systems and also validated in accordance with the famed weyker’s properties. The proposed analyzability metric here is intended to lead the new developments in object-oriented software maintainability parameter in future and help the new researchers do their research the right way thus eventually achieving the quality aspect of the objectoriented software system and fulfilling the needs of the users.
This work aims to provide a practical guide to assist students of Computer Science
courses and related fields to conduct a systematic literature review. The steps proposed
in this paper to conduct a systematic review were extracted from a technical report
published by the researcher Bárbara Kitchenham [1] and arranged in a more objective
format
IRJET - Student Pass Percentage Dedection using Ensemble LearninngIRJET Journal
This document discusses using ensemble learning methods to predict student pass rates. It begins with an abstract describing ensemble learning and its applications. It then provides background on strengthening the STEM workforce and using prediction modeling in educational data mining. The methodology section describes using decision trees, logistic regression, nearest neighbors, neural networks, naive Bayes, and support vector machines as base classifiers in an ensemble model to predict student enrollment in STEM courses. The results show the J48 decision tree algorithm correctly classified 84% of instances, outperforming naive Bayes and CART. The conclusion is that ensemble models can better categorize factors affecting student choice to enroll in STEM by combining multiple classification techniques.
A Novel approach for Document Clustering using Concept ExtractionAM Publications
In this paper we present a novel approach to extract the concept from a document and cluster such set of documents depending on the concept extracted from each of them. We transform the corpus into vector space by using term frequency–inverse document frequency then calculate the cosine distance between each document, followed by clustering them using K means algorithm. We also use multidimensional scaling to reduce the dimensionality within the corpus. It results in the grouping of documents which are most similar to each other with respect to their content and the genre.
Maturing software engineering knowledge through classificationsimmortalchhetri
The document provides an overview of research conducted on classifying unit testing techniques. It discusses previous work on testing technique classifications and identifies their limitations. The research aims to develop a new classification scheme using operational and historical criteria. 30 classification criteria are identified and used to analyze 13 testing techniques. The results show the new classification improves efficiency and usability when selecting techniques compared to previous schemes. Knowledge gaps in existing techniques are also examined.
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1. European Journal of Molecular & Clinical Medicine
ISSN 2515-8260 Volume 07, Issue 09, 2020
2231
AUTOMATIC CLASSIFICATION AND EXTRACTION OF NON-FUNCTIONAL
REQUIREMENTS FROM TEXT FILES: A SUPERVISED LEARNING APPROACH
Vatchala. Sa
, Bingi Manorama Devib
, M. Sharmila Devic
, Sathish. Ad
a
Research Scholar, Anna University, Chennai, India
b
Assistant Professor, Department of CSE, KSRM College of Engineering, Kadapa, AP, India.
c
Assistatnt Professor, Department of CSE, Santhiram Engineering College, Nandyal, AP, India
d
Research Scholar, Anna University, Chennai, India
Address for Correspondence
Vatchala. S
Research Scholar, Anna University, Chennai, India
Abstract: Non-functional requirements play a critical role in choosing various alternative model
and ultimate implementation criteria. It is extremely significant in the earlier stages of software
development that requirement engineering produces successful technology and eliminates system
failure. The recent work has shown that the automated extraction and classification of quality
attributes from text files have been demonstrated by artificial intelligence approaches including
machine learning and text mining. In the automated extraction and classification of non-
functional specifications, we suggest a supervised categorization approach. To test our approach
to obtain interesting outcomes, a very well-known dataset is used. In terms of security and
performance, we obtained a specific range of 85% to 98% and obtained a best result together for
security, performance and usability.
Keywords: Non Functional requirement, Machine Learning, Artificial intelligence.
1 Introduction
The non-functional requirements describe significant software development and software system
behavior. They offer a variety of quality characteristics for developed software. Such
characteristics play an important part in architectural design and should therefore be taken into
account as early as possible during the review process. A basic question is how to find out what
users really need in Requirements Engineering (RE). A vital aspect of software development
projects is the achievement of the required specifications. The most important of the RE-
recommended activities that resulted are the successful elicitation of requirements. The
application of software engineering artificial intelligence techniques (AI&SE) has provided some
promising results. Over the last years, research has shown that artificial intelligence automates
the extraction and classification through techniques, including machine learning and text mining,
of quality attributes from text documents. The most widely cited data collection has currently
2. European Journal of Molecular & Clinical Medicine
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been derived from the 15 specifications of master’s degree (M) projects for requirement
engineering at DePaul University and first appears in 2006 literature for automated classification
of non‐functional criteria (NFR) with supervised learning techniques. In the course of writing the
specifications, NFR analysis was carried out by the students themselves and the method of
identifying the data set missed logic.
2. Related work
The literature survey is mainly done by taking deep insight into different literature sources and
the following literature review is focus on different parameters and methods that are used
extraction and classification of NFR from text files: a supervised learning approach. Zhang, Wen,
et al [2]Throughout this analysis, vector space and machine learning techniques were
implemented to automatically classify NFR, to convert NFRs into numerical vectors of specific
index words and to analyse their output in the automatic classification of NFRs.The machine
learning classification used in this journal is Support vector machine with a linear kernel, a
classification that is strongly advisable for text mining. Additional information is provided for
choosing apps and Support vector machines for automated sorting. Experiments have been
carried out using the PROMISE data set obtained.
Jane Cleland-Huang et al [3] A modern methodology has been implemented focused on
knowledge recovery approaches to identify and recognize non-functional criteria, both in formal
specifications and free text formats. Even though an observer is always expected to determine the
consistency of the nominee NFR, it may involve less work as for example the Theme / Doc
approach than before semi-automated classification techniques. It’s helpful for software
developers to plan, develop and evaluate a software framework to be able to distinguish NFRs in
this manner, as this will allow them to consider stakeholders' desires and to see consistency
between different device restrictions. The NFRs may be identified by and marked as
intrusiveness in architectural design meetings or collected from a security expert by evaluating
particular security issues, while the applicant can receive interviews or minutes from
stakeholders. Aspects of applicants should be determined early in the selection phase, thereby
raising the expense of recovery measures which might otherwise have existed. For example, an
emerging method built at the University of DePaul integrates NFR identification into a shared
modeling framework for a broad variety of non-functional objectives. The NFR method
perspectives provide important insight into the modeling phase.
Slankas et al[1] Presented the latest NFR Locator method and a resource to support researchers
retrieve specific non-functional requirements easily from a broad range of records. A collection
of system-related records in the health care sector for NFR detection and extraction have been
compiled to test the device.
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3. Methodology
In an actual world scenario, the research team in the field of software development will
review all necessary documents collected by an application elicitation team in order to decide
whether a software development project is prioritized and mapped with the most important
category or classes of NFR (e.g. security availability, scalability, etc. This method takes time and
analysts need to collaborate as each application document needs to be interpreted and classified
manually. For detection and classification of non-functional requirements in a series of defined
categories or groups a supervised text- approach is suggested.
We establish a structural and light-weight mapping method to support the development of a
SIG catalog, following the guidance and guidelines from Petersen, Vakkalanka and Kuzney. The
feasibility of classification of non-functional requirements within the proposed dataset was
evaluated and categorized. The processing of the data is facilitated by the data set. The data set is
established. There are three tasks involved in this systematic mapping: i)Planning of a search
string ii) defining the filtration criteria and iii) Analyzing papers. It is as follows:Search string
planning, the purpose of this project is to develop check list for indexing research document sites
used in NFR catalog articles. The selected portals are the IEEE and Science Direct. We use
search string specification keywords for NFR catalogues like "NFR Framework" and "Chung"
and Paper review we integrate this with certain types of non-functional criteria such as "non-
function parameters" and "non-functional," in order to achieve a search string version 1 (see
Table 1).
When we first looked at portals, we noticed articles that contained NFR catalogs and used index
words on the web chain to create a new edition of the application channel 2 (see Table I). The
number of papers found after search definition has increased, because the search chain has
increased. In the NFR Catalog division, we introduced terms expanding the spectrum of scope,
including: SIG and catalogues.
Table 1. Search string evolution
Search String V1 portal Papers
(“nfr” or “nfr framework” or
“chung”) or (“non-functional
Requirements” or “non-functional”)
IEEE 1553
Science direct 1045
Total 2598
Search String V2
4. European Journal of Molecular & Clinical Medicine
ISSN 2515-8260 Volume 07, Issue 09, 2020
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The purpose
of this
experiment is to finding filters for indexing portals to enhance content in identified papers and
eventually to create standards for inclusion and exclusion after search strings have been drawn up
for review. We used two filters in the mapping test; the first one was year round. Between 2000
to 2018 we only chose papers because in 2000 the NFR catalogs were introduced. The second
filter applies to Appendix A's conventions and papers.
Table 2. Result after filters
Filter Portals Papers
Year+
Conferences
And Journals
IEEE 193
Science Direct 392
Total papers 585
The number of publications dropped from 6636 to 585 after filters were added. (see Table 2).
The next move is to introduce the criterion for incorporation and elimination.The minimum
requirement was to include the document as NFR catalogs to be included and thus the rejection
condition is omission. The object of the inclusion and exclusion criterion is to choose the
documents to be included during the paper review stage.
The last phase of the analysis is the paper. This research aims to analyze the results of the
dependent study, which discuss the consistency of the NFR catalogs in every document. We have
chosen a selection of 20 articles for this lightweight systematic mapping. The documents were
picked because the NFR catalogs represent a number of soft targets(in terms of security,
performance and usability). In terms of the table, we prefer exceptionally informative SIG
catalogues. This makes a broader variety of words in our dataset.
Once the comprehensive "lightweight" mapping has been done, we build a data set that collects
keywords in 31 catalogs. In the purpose of creating a data collection there were two activities.
The first step is to categorize keywords. The category of database specified each keyword in the
NFR catalogs.
(“nfr” or “nfrframework”or
“chung” or “sig”or “catalouge”) and
(“non- functional requirements” or
“non-
Functional” or “quality attribute”
IEEE 2882
Science direct 3754
Total 6636
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For eg, keywords were connected in many protection catalogs to the exclusion of repetitive
keywords: the keyword is sensitive so a keyword for our data set was only entered once. For
example, a keyword was added for several of our data catalogs. The final dataset comprises 77
words divided into: usability of 28 words, security of 24 words and performance of 25 words see
Table 6. For RQ2, a systemic process has been built for defining the dataset consisting
essentially of four activities according to Figure 2.
Fig 1.The Dataset Definition Process.
This type of method has four operations: i) Systematic "lightweight" process visualization, (ii)
Converting the sig parser into csv format (iii) merging keywords (iv) Level of quality assurance.
The first element of the hierarchical approach was previously described in which we showed in
the systematic mapping review the "lightweight" of the three operations. The next move was to
create a CSV parser SIG, which should be translated into a CSV format as the principal objective
of this project.
Fig 3.SIG Catalogue Performance
Table 3. Performance keywords
NFR Type Keywords
Performance Performance, time, index, triggers, response
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These SIG catalogs were produced using StarUML3 and RE-Tools. For each SIG catalog a
parser was developed and implemented. The method is configured in three phases: (i) determine
the SIG root objective of the NFR category that the catalog contains. (ii) After that the NFR was
classified by all keywords found in its branches. And (iii) Creating a CSV file with keywords is
the final step. Table 3 and Table 4 shows the collection of keywords produced during this
procedure and the two product catalogs, as seen in Figures 3 and 4.
Fig 4. SIG Catalogue Performance
Table 4. Performance keywords
NFR Type Keywords
Performance Performance, space, time, memory,
Throughput, response, peak
Keyword combine is the third process. This method mostly includes
generating a CSV file containing a collection of numerous keywords for each form of NFR from
the previous level. Keywords: answer and time won't be included in the new set of keywords, as
they have been introduced beforeTable 5 shows the final collection of keywords after the merge
phase.
Table 5.Performance keywords merge
NFR
Type
Keywords
Performance Performance, space, memory, time,
throughput, reponse, Triggers, index, peak
7. European Journal of Molecular & Clinical Medicine
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Table 6 shows the collection of keywords contained after operations ii and iii in all the SIG
catalogs. Following the fusion phase in the 24 security catalogues, Fig5 displays the actual SIG
catalog.
Table 6. Keywords extracted from sig catalogues
NFR Type Keywords
performance
performance, space, time, throughput, response,
memory, consumption, fast, index, triggers, storage, low,
run, runtime, perform, execute, mean, peak, compress,
dynamic, offset, reduce, fixing, early, processing
Security
security, confidentiality, integrity, availability, accuracy,
completeness, secure, access, registration, ,
authorization, identification,authentication, validation,
transaction, user, password, control, encryption, key,
spoofing, attack, policy, logging, permission.
Usability
Utility, Homogeneity, Easiness,Operationability,
intuitively, adaptable, Understandability, accessible,
Configuration,Integrity, Administration, Conformity,
cognizance, applicable, Linguistics, supportive, tutorials,
trainable, helping, flexible, easy, usable, Graphics,
timing.
The last process in the data selection system is quality assurance. (Figure 2). The first dataset
collected in this operation is where the efficiency of research is checked and the classification
accuracy is observed.The consequence of this method is the coherence of the terms collected in
such catalogues. They use an adjective that makes sense in a collective setting, to which the word
refers, for every phrase in the foundation.For examples, the term convenience generates
adjectives in the category of usability: simple and quick; for security category we obtain
adjectives such as privacy and secrecy use the word confidentiality.
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Fig 5 : The final SIG Security Catalogue.
4. Dataset
For the assessment of the proposed technique a previously identified dataset with non-functional
criteria is needed. We have chosen an Open Science Tera-PROMISE dataset comprising 625
specifications, either functionally or not. It is described in this paper as a promise dataset.
Dividing into 11 types of non- criteria. Such parameters were originally obtained from 15
project specifications of a master degree in engineering requirements from DePaul University.
An initial non-criteria selection of more phrases per group was carried out to verify the strategy:
security (66 phrases), performance (54 phrases), usability (67 phrasses) and operational (62
phrases). The operating class is omitted since there is no relation to an existing NFR catalog. The
result was 187 phrases regarding security, performance and usability.
5. Conclusion
The right requirements are considered an important and complex step in the creation of software
projects. The work has demonstrated in recent years that artificial technology automatically
extracts and classifies the content properties of textual records utilizing strategies including
machine learning and text mining.
This paper introduces a new way of defining non-functional criteria using NFR catalogs by
means of machine learning algorithms and offers a way of accessing such catalogs utilizing the
structural "light weight mapping" technique.
One of the massive problems to solve this step searching is trying to define the training data set
performing the criteria manual assignment, which will increase effort. Such classification
distinguishes between the generated data, such that machine learning is still partial because the
managed algorithms require such dataset to be categorized. Their analysis provides a way of
generating data sets used to classify non-functional specifications by NFR catalogs extracted
from the research of mapping in order to address this circumstance.
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References
1 Slankas, John, and Laurie Williams. "Automated extraction of non-functional
requirements in available documentation." 2013 1st International Workshop on
Natural Language Analysis in Software Engineering (NaturaLiSE). IEEE, 2013
2 Zhang, Wen, et al. "An empirical study on classification of non-functional
requirements." The twenty-third international conference on software engineering and
knowledge engineering (SEKE 2011). 2011.
3 J. Cleland-Huang, R. Settimi, X. Zou, and P. Solc. The detection andclassification of
non-functional requirements with application to earlyaspects. In Requirements
Engineering, 14th IEEE InternationalConference. IEEE, 2006.