Software companies spend over 45 percent of cost in dealing with software bugs. An inevitable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new bug.
Towards Effective Bug Triage with Software Data Reduction Techniques1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Towards Effective Bug Triage with Software Data Reduction Techniques1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Knowledge and Data Engineering IEEE 2015 ProjectsVijay Karan
List of Knowledge and Data Engineering IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Knowledge and Data Engineering for the year 2015
TOWARDS PREDICTING SOFTWARE DEFECTS WITH CLUSTERING TECHNIQUESijaia
The purpose of software defect prediction is to improve the quality of a software project by building a
predictive model to decide whether a software module is or is not fault prone. In recent years, much
research in using machine learning techniques in this topic has been performed. Our aim was to evaluate
the performance of clustering techniques with feature selection schemes to address the problem of software
defect prediction problem. We analysed the National Aeronautics and Space Administration (NASA)
dataset benchmarks using three clustering algorithms: (1) Farthest First, (2) X-Means, and (3) selforganizing map (SOM). In order to evaluate different feature selection algorithms, this article presents a
comparative analysis involving software defects prediction based on Bat, Cuckoo, Grey Wolf Optimizer
(GWO), and particle swarm optimizer (PSO). The results obtained with the proposed clustering models
enabled us to build an efficient predictive model with a satisfactory detection rate and acceptable number
of features.
Associative Regressive Decision Rule Mining for Predicting Customer Satisfact...csandit
Opinion mining also known as sentiment analysis, involves customer satisfactory patterns,
sentiments and attitudes toward entities, products, services and their attributes. With the rapid
development in the field of Internet, potential customer’s provides a satisfactory level of
product/service reviews. The high volume of customer reviews were developed for
product/review through taxonomy-aware processing but, it was difficult to identify the best
reviews. In this paper, an Associative Regression Decision Rule Mining (ARDRM) technique is
developed to predict the pattern for service provider and to improve customer satisfaction based
on the review comments. Associative Regression based Decision Rule Mining performs twosteps
for improving the customer satisfactory level. Initially, the Machine Learning Bayes
Sentiment Classifier (MLBSC) is used to classify the class labels for each service reviews. After
that, Regressive factor of the opinion words and Class labels were checked for Association
between the words by using various probabilistic rules. Based on the probabilistic rules, the
opinion and sentiments effect on customer reviews, are analyzed to arrive at specific set of
service preferred by the customers with their review comments. The Associative Regressive
Decision Rule helps the service provider to take decision on improving the customer satisfactory
level. The experimental results reveal that the Associative Regression Decision Rule Mining
(ARDRM) technique improved the performance in terms of true positive rate, Associative
Regression factor, Regressive Decision Rule Generation time and Review Detection Accuracy of
similar pattern.
Bug triage means to transfer a new bug to expertise developer. The manual bug triage is opulent in time
and poor in accuracy, there is a need to automatize the bug triage process. In order to automate the bug triage
process, text classification techniques are applied using stopword removal and stemming. In our proposed work
we have used NB-Classifiers to predict the developers. The data reduction techniques like instance selection
and keyword selection are used to obtain bug report and words. This will help the system to predict only those
developers who are expertise in solving the assigned bug. We will also provide the change of status of bug
report i.e. if the bug is solved then the bug report will be updated. If a particular developer fails to solve the bug
then the bug will go back to another developer.
Machine Learning approaches are good in solving problems that have less information. In most cases, the
software domain problems characterize as a process of learning that depend on the various circumstances
and changes accordingly. A predictive model is constructed by using machine learning approaches and
classified them into defective and non-defective modules. Machine learning techniques help developers to
retrieve useful information after the classification and enable them to analyse data from different
perspectives. Machine learning techniques are proven to be useful in terms of software bug prediction. This
study used public available data sets of software modules and provides comparative performance analysis
of different machine learning techniques for software bug prediction. Results showed most of the machine
learning methods performed well on software bug datasets.
Knowledge and Data Engineering IEEE 2015 ProjectsVijay Karan
List of Knowledge and Data Engineering IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Knowledge and Data Engineering for the year 2015
A sexta lei espiritual, do sucesso consiste na Lei do Desprendimento. A Lei do Desprendimento diz-nos que para, adquirirmos qualquer coisa no universo físico temos de renunciar à nossa ligação a ela. Isto não significa que desistamos da intenção de criar o desejo.
AUTOMATED BUG TRIAGE USING ADVANCED DATA REDUCTION TECHNIQUESJournal For Research
Bug triage is an important step in the process of bug fixing. The goal of bug triage is to correctly assign a developer to a newly reported bug in the system. To perform the automated bug triage, text classification techniques are applied. This will helps to reduce the time cost in manual work. To reduce the scale and improve the quality of bug data, the proposed system addresses the data reduction techniques, instance selection and feature selection for bug triage. The instance selection technique used here is to identify the relevant bugs that can match the newly reported bug. The feature selection technique is used to select the relevant data from each bug in the training set. A predictive model is proposed to identify the order in which the data reduction techniques are applied for each newly reported bug. This step will improve the performance of the classification process. An experimental study using Eclipse and Firefox bug data is undergone in which the proposed system shows an accuracy of 73%.
Knowledge and Data Engineering IEEE 2015 ProjectsVijay Karan
List of Knowledge and Data Engineering IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Knowledge and Data Engineering for the year 2015
TOWARDS PREDICTING SOFTWARE DEFECTS WITH CLUSTERING TECHNIQUESijaia
The purpose of software defect prediction is to improve the quality of a software project by building a
predictive model to decide whether a software module is or is not fault prone. In recent years, much
research in using machine learning techniques in this topic has been performed. Our aim was to evaluate
the performance of clustering techniques with feature selection schemes to address the problem of software
defect prediction problem. We analysed the National Aeronautics and Space Administration (NASA)
dataset benchmarks using three clustering algorithms: (1) Farthest First, (2) X-Means, and (3) selforganizing map (SOM). In order to evaluate different feature selection algorithms, this article presents a
comparative analysis involving software defects prediction based on Bat, Cuckoo, Grey Wolf Optimizer
(GWO), and particle swarm optimizer (PSO). The results obtained with the proposed clustering models
enabled us to build an efficient predictive model with a satisfactory detection rate and acceptable number
of features.
Associative Regressive Decision Rule Mining for Predicting Customer Satisfact...csandit
Opinion mining also known as sentiment analysis, involves customer satisfactory patterns,
sentiments and attitudes toward entities, products, services and their attributes. With the rapid
development in the field of Internet, potential customer’s provides a satisfactory level of
product/service reviews. The high volume of customer reviews were developed for
product/review through taxonomy-aware processing but, it was difficult to identify the best
reviews. In this paper, an Associative Regression Decision Rule Mining (ARDRM) technique is
developed to predict the pattern for service provider and to improve customer satisfaction based
on the review comments. Associative Regression based Decision Rule Mining performs twosteps
for improving the customer satisfactory level. Initially, the Machine Learning Bayes
Sentiment Classifier (MLBSC) is used to classify the class labels for each service reviews. After
that, Regressive factor of the opinion words and Class labels were checked for Association
between the words by using various probabilistic rules. Based on the probabilistic rules, the
opinion and sentiments effect on customer reviews, are analyzed to arrive at specific set of
service preferred by the customers with their review comments. The Associative Regressive
Decision Rule helps the service provider to take decision on improving the customer satisfactory
level. The experimental results reveal that the Associative Regression Decision Rule Mining
(ARDRM) technique improved the performance in terms of true positive rate, Associative
Regression factor, Regressive Decision Rule Generation time and Review Detection Accuracy of
similar pattern.
Bug triage means to transfer a new bug to expertise developer. The manual bug triage is opulent in time
and poor in accuracy, there is a need to automatize the bug triage process. In order to automate the bug triage
process, text classification techniques are applied using stopword removal and stemming. In our proposed work
we have used NB-Classifiers to predict the developers. The data reduction techniques like instance selection
and keyword selection are used to obtain bug report and words. This will help the system to predict only those
developers who are expertise in solving the assigned bug. We will also provide the change of status of bug
report i.e. if the bug is solved then the bug report will be updated. If a particular developer fails to solve the bug
then the bug will go back to another developer.
Machine Learning approaches are good in solving problems that have less information. In most cases, the
software domain problems characterize as a process of learning that depend on the various circumstances
and changes accordingly. A predictive model is constructed by using machine learning approaches and
classified them into defective and non-defective modules. Machine learning techniques help developers to
retrieve useful information after the classification and enable them to analyse data from different
perspectives. Machine learning techniques are proven to be useful in terms of software bug prediction. This
study used public available data sets of software modules and provides comparative performance analysis
of different machine learning techniques for software bug prediction. Results showed most of the machine
learning methods performed well on software bug datasets.
Knowledge and Data Engineering IEEE 2015 ProjectsVijay Karan
List of Knowledge and Data Engineering IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Knowledge and Data Engineering for the year 2015
A sexta lei espiritual, do sucesso consiste na Lei do Desprendimento. A Lei do Desprendimento diz-nos que para, adquirirmos qualquer coisa no universo físico temos de renunciar à nossa ligação a ela. Isto não significa que desistamos da intenção de criar o desejo.
AUTOMATED BUG TRIAGE USING ADVANCED DATA REDUCTION TECHNIQUESJournal For Research
Bug triage is an important step in the process of bug fixing. The goal of bug triage is to correctly assign a developer to a newly reported bug in the system. To perform the automated bug triage, text classification techniques are applied. This will helps to reduce the time cost in manual work. To reduce the scale and improve the quality of bug data, the proposed system addresses the data reduction techniques, instance selection and feature selection for bug triage. The instance selection technique used here is to identify the relevant bugs that can match the newly reported bug. The feature selection technique is used to select the relevant data from each bug in the training set. A predictive model is proposed to identify the order in which the data reduction techniques are applied for each newly reported bug. This step will improve the performance of the classification process. An experimental study using Eclipse and Firefox bug data is undergone in which the proposed system shows an accuracy of 73%.
USING CATEGORICAL FEATURES IN MINING BUG TRACKING SYSTEMS TO ASSIGN BUG REPORTSijseajournal
Most bug assignment approaches utilize text classification and information retrieval techniques. These
approaches use the textual contents of bug reports to build recommendation models. The textual contents of
bug reports are usually of high dimension and noisy source of information. These approaches suffer from
low accuracy and high computational needs. In this paper, we investigate whether using categorical fields
of bug reports, such as component to which the bug belongs, are appropriate to represent bug reports
instead of textual description. We build a classification model by utilizing the categorical features, as a
representation, for the bug report. The experimental evaluation is conducted using three projects namely
NetBeans, Freedesktop, and Firefox. We compared this approach with two machine learning based bug
assignment approaches. The evaluation shows that using the textual contents of bug reports is important. In
addition, it shows that the categorical features can improve the classification accuracy
A NOVEL APPROACH TO ERROR DETECTION AND CORRECTION OF C PROGRAMS USING MACHIN...IJCI JOURNAL
There has always been a struggle for programmers to identify the errors while executing a program- be it
syntactical or logical error. This struggle has led to a research in identification of syntactical and logical
errors. This paper makes an attempt to survey those research works which can be used to identify errors as
well as proposes a new model based on machine learning and data mining which can detect logical and
syntactical errors by correcting them or providing suggestions. The proposed work is based on use of
hashtags to identify each correct program uniquely and this in turn can be compared with the logically
incorrect program in order to identify errors.
Software testing defect prediction model a practical approacheSAT Journals
Abstract Software defects prediction aims to reduce software testing efforts by guiding the testers through the defect classification of software systems. Defect predictors are widely used in many organizations to predict software defects in order to save time, improve quality, testing and for better planning of the resources to meet the timelines. The application of statistical software testing defect prediction model in a real life setting is extremely difficult because it requires more number of data variables and metrics and also historical defect data to predict the next releases or new similar type of projects. This paper explains our statistical model, how it will accurately predict the defects for upcoming software releases or projects. We have used 20 past release data points of software project, 5 parameters and build a model by applying descriptive statistics, correlation and multiple linear regression models with 95% confidence intervals (CI). In this appropriate multiple linear regression model the R-square value was 0.91 and its Standard Error is 5.90%. The Software testing defect prediction model is now being used to predict defects at various testing projects and operational releases. We have found 90.76% precision between actual and predicted defects.
Application of Genetic Algorithm in Software Engineering: A ReviewIRJESJOURNAL
Abstract. The software engineering is comparatively new and regularly changing field. The big challenge of meeting strict project schedules with high quality software requires that the field of software engineering be automated to large extent and human resource intervention be minimized to optimum level. To achieve this goal the researcher have explored the potential of machine learning approaches as they are adaptable, have learning ability. In this paper, we take a look at how genetic algorithm (GA) can be used to build tool for software development and maintenance tasks.
54 C o m m u n i C at i o n s o F t h e a C m | j u Ly 2 0 1 2 | v o L . 5 5 | n o . 7
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A r e s o f T wA r e M e T r i C s helpful tools or a waste of time?
For every developer who treasures these
mathematical abstractions of software systems
there is a developer who thinks software metrics are
invented just to keep project managers busy. Software
metrics can be very powerful tools that help achieve
your goals but it is important to use them correctly, as
they also have the power to demotivate project teams
and steer development in the wrong direction.
For the past 11 years, the Software Improvement
Group has advised hundreds of organizations
concerning software development and risk
management on the basis of software metrics.
We have used software metrics in more than 200
investigations in which we examined a single snapshot
of a system. Additionally, we use software metrics to
track the ongoing development effort of more than
400 systems. While executing these projects, we have
learned some pitfalls to avoid when using software
metrics in a project management setting. This
article addresses the four most important of these:
˲ Metric in a bubble;
˲ Treating the metric;
˲ One-track metric; and
˲ Metrics galore.
Knowing about these pitfalls will
help you recognize them and, hopeful-
ly, avoid them, which ultimately leads
to making your project successful. As
a software engineer, your knowledge
of these pitfalls helps you understand
why project managers want to use soft-
ware metrics and helps you assist the
managers when they are applying met-
rics in an inefficient manner. As an
outside consultant, you need to take
the pitfalls into account when pre-
senting advice and proposing actions.
Finally, if you are doing research in
the area of software metrics, knowing
these pitfalls will help place your new
metric in the right context when pre-
senting it to practitioners. Before div-
ing into the pitfalls, let’s look at why
software metrics can be considered a
useful tool.
software metrics steer People
“You get what you measure.” This
phrase definitely applies to software
project teams. No matter what you de-
fine as a metric, as soon as it is used to
evaluate a team, the value of the metric
moves toward the desired value. Thus,
to reach a particular goal, you can con-
tinuously measure properties of the
desired goal and plot these measure-
ments in a place visible to the team.
Ideally, the desired goal is plotted
alongside the current measurement to
indicate the distance to the goal.
Imagine a project in which the run-
time performance of a particular use
case is of critical importance. In this
case it helps to create a test in which
the execution time of the use case is
measured daily. By plotting this daily
data point against the desired value,
and making sure the team sees this
mea.
A Personal Privacy Data Protection Scheme for Encryption and Revocation of Hi...Shakas Technologies
A Personal Privacy Data Protection Scheme for Encryption and Revocation of High-Dimensional Attri
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
Detecting Mental Disorders in social Media through Emotional patterns-The cas...Shakas Technologies
Detecting Mental Disorders in social Media through Emotional patterns-The case of Anorexia and depression
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
CO2 EMISSION RATING BY VEHICLES USING DATA SCIENCE
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
Identifying Hot Topic Trends in Streaming Text Data Using News Sequential Evo...Shakas Technologies
Identifying Hot Topic Trends in Streaming Text Data Using News Sequential Evolution Model Based on Distributed Representations.
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
When stars align: studies in data quality, knowledge graphs, and machine lear...
Towards effective bug triage with software
1. 1.
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TOWARDS EFFECTIVE BUG TRIAGE WITH SOFTWARE
DATA REDUCTION TECHNIQUES
ABSTRACT
Software companies spend over 45 percent of cost in dealing with software bugs. An
inevitable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new
bug. To decrease the time cost in manual work, text classification techniques are applied to
conduct automatic bug triage. In this paper, we address the problemof data reduction for bug
triage, i.e., how to reduce the scale and improve the quality of bug data. We combine instance
selection with feature selection to simultaneously reduce data scale on the bug dimension and the
word dimension. To determine the order of applying instance selection and feature selection, we
extract attributes from historical bug data sets and build a predictive model for a new bug data
set. We empirically investigate the performance of data reduction on totally 600,000 bug reports
of two large open source projects, namely Eclipse and Mozilla. The results show that our data
reduction can effectively reduce the data scale and improve the accuracy of bug triage. Our work
provides an approach to leveraging techniques on data processing to form reduced and high-
quality bug data in software development and maintenance.
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EXISTING SYSTEM:
We review existing work on modeling bug data, bug triage, and the quality of bug data
with defect prediction. 7.1 Modeling Bug Data To investigate the relationships in bug data,
Sandusky et al. form a bug report network to examine the dependency among bug reports.
Besides studying relationships among bug reports, Hong et al. build a developer social network
to examine the collaboration among developers based on the bug data in Mozilla project. This
developer social network is helpful to understand the developer community and the project
evolution. By mapping bug priorities to developers, Xuan et al. identify the developer
prioritization in open source bug repositories. The developer prioritization can distinguish
developers and assist tasks in software maintenance. Bug Triage Bug triage aims to assign an
appropriate developer to fix a new bug, i.e., to determine who should fix a bug. _Cubrani_c and
Murphy first propose the problem of automatic bug triage to reduce the cost of manual bug
triage. They apply text classification techniques to predict related developers. Anvik et al.
examine multiple techniques on bug triage, including data preparation and typical classifiers.
Anvik and Murphy extend above work to reduce the effort of bug triage by creating
development-oriented recommenders. Jeong et al. find out that over 37 percent of bug reports
have been reassigned in manual bug triage. They propose a tossing graph method to reduce
reassignment in bug triage. To avoid low-quality bug reports in bug triage, Xuan et al. train a
semi-supervised classifier by combining unlabeled bug reports with labeled ones. Park et al.
convert bug triage into an optimization problem and propose a collaborative filtering approach to
reducing the bugfixing time.
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PROPOSED SYSTEM:
The primary contributions of this paper are as follows:
1) We present the problem of data reduction for bug triage. This problem aims to augment the
data set of bug triage in two aspects, namely a) to simultaneously reduce the scales of the bug
dimension and the word dimension and b) to improve the accuracy of bug triage.
2) We propose a combination approach to addressing the problem of data reduction. This can be
viewed as an application of instance selection and feature selection in bug repositories.
3) We build a binary classifier to predict the order of applying instance selection and feature
selection. To our knowledge, the order of applying instance selection and feature selection has
not been investigated in related domains. This paper is an extension of our previous work. In this
extension, we add new attributes extracted from bug data sets, prediction for reduction orders,
and experiments on four instance selection algorithms, four feature selection algorithms, and
their combinations In this paper, we address the problem of data reduction for bug triage, i.e.,
how to reduce the bug data to save the labor cost of developers and improve the quality to
facilitate the process of bug triage. Data reduction for bug triage aims to build a small-scale and
high-quality set of bug data by removing bug reports and words, which are redundant or non-
informative. In our work, we combine existing techniques of instance selection and feature
selection to simultaneously reduce the bug dimension and the word dimension. The reduced bug
data contain fewer bug reports and fewer words than the original bug data and provide similar
information over the original bug data. We evaluate the reduced bug data according to two
criteria: the scale of a data set and the accuracy of bug triage. To avoid the bias of a single
algorithm, we empirically examine the results of four instance selection algorithms and four
feature selection algorithms.
4. 1.
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Module 1
Data reduction
Data reduction is the transformation of numerical or alphabetical digital information derived
empirically or experimentally into a corrected, ordered, and simplified form. The basic concept is
the reduction of multitudinous amounts of data down to the meaningful parts. When information
is derived from instrument readings there may also be a transformation from analog to digital
form. When the data are already in digital form the 'reduction' of the data typically involves some
editing, scaling, coding, sorting, collating, and producing tabular summaries. When the
observations are discrete but the underlying phenomenon is continuous then smoothing and
interpolation are often needed. Often the data reduction is undertaken in the presence of reading
or measurement errors. Some idea of the nature of these errors is needed before the most likely
value may be determined.
Module 2
Benefit of Data Reduction
In our work, to save the labor cost of developers, the data reduction for bug triage has two goals,
1) reducing the data scale and 2) improving the accuracy of bug triage. In contrast to modeling
the textual content of bug reports in existing work, we aim to augment the data set to build a
preprocessing approach, which can be applied before an existing bug triage approach. We
explain the two goals of data reduction as follows. Reducing the Data Scale - We reduce scales
of data sets to save the labor cost of developers. Bug dimension.The aim of bug triage is to
assign developers for bug fixing. Once a developer is assigned to a new bug report, the developer
can examine historically fixed bugs to form a solution to the current bug report. For example,
historical bugs are checked to detect whether the new bug is the duplicate of an existing one;
moreover, existing solutions to bugs can be searched and applied to the new bug . Thus, we
consider reducing duplicate and noisy bug reports to decrease the number of historical bugs. In
practice, the labor cost of developers (i.e., the cost of examining historical bugs) can be saved by
decreasing the number of bugs based on instance selection. Word dimension. We use feature
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selection to remove noisy or duplicate words in a data set. Based on feature selection, the
reduced data set can be handled more easily by automatic techniques (e.g., bug triage
approaches) than the original data set. Besides bug triage, the reduced data set can be further
used for other software tasks after bug triage (e.g., severity identification, time prediction, and
reopened bug analysis).
Improving the Accuracy - Accuracy is an important evaluation criterion for bug triage. In our
work, data reduction explores and removes noisy or duplicate information in data sets. Bug
dimension. Instance selection can remove uninformative bug reports; meanwhile, we can observe
that the accuracy may be decreased by removing bug reports. Word dimension By removing
uninformative words, feature selection improves the accuracy of bug triage. This can recover the
accuracy loss by instance selection.
Module3
Data reduction for bug triage
We propose bug data reduction to reduce the scale and to improve the quality of data in bug
repositories. We combine existing techniques of instance selection and feature selection to
remove certain bug reports and words. A problem for reducing the bug data is to determine the
order of applying instance selection and feature selection, which is denoted as the prediction of
reduction orders. In this section, we first present how to apply instance selection and feature
selection to bug data, i.e., data reduction for bug triage. Then, we list the benefit of the data
reduction.
Module 4
Applying Instance Selection and Feature Selection
In bug triage, a bug data set is converted into a text matrix with two dimensions, namely the bug
dimension and the word dimension. In our work, we leverage the combination of instance
selection and feature selection to generate a reduced bug data set. We replace the original data
set with the reduced data set for bug triage. Instance selection and feature selection are widely
used techniques in data processing. For a given data set in a certain application, instance
6. 1.
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selection is to obtain a subset of relevant instances (i.e., bug reports in bug data) while feature
selection aims to obtain a subset of relevant features (i.e., words in bug data). In our work, we
employ the combination of instance selection and feature selection. To distinguish the orders of
applying instance selection and feature selection, we give the following denotation. Given an
instance selection algorithm IS and a feature selection algorithm FS, we use FS!IS to denote the
bug data reduction, which first applies FS and then IS; on the other hand, IS!FS denotes first
applying IS and then FS. In Algorithm 1, we briefly present how to reduce the bug data based on
FS ! IS. Given a bug data set, the output of bug data reduction is a new and reduced data set.
Two algorithms FS and IS are applied sequentially. Note that in Step 2), some of bug reports
may be blank during feature selection, i.e., all the words in a bug report are removed. Such blank
bug reports are also removed in the feature selection.
Module 5
Reduction Orders
To apply the data reduction to each new bug data set, we need to check the accuracy of both two
orders (FS ! IS and IS!FS) and choose a better one. To avoid the time cost of manually checking
both reduction orders, we consider predicting the reduction order for a new bug data set based on
historical data sets. We convert the problem of prediction for reduction orders into a binary
classification problem. A bug data set is mapped to an instance and the associated reduction
order (either FS ! IS or IS ! FS) is mapped to the label of a class of instances. Note that a
classifier can be trained only once when facing many new bug data sets. That is, training such a
classifier once can predict the reduction orders for all the new data sets without checking both
reduction orders. To date, the problem of predicting reduction orders of applying feature
selection and instance selection has not been investigated in other application scenarios. From
the perspective of software engineering, predicting the reduction order for bug data sets can be
viewed as a kind of software metrics, which involves activities for measuring some property for
a piece of software. However, the features in our work are extracted from the bug data set while
the features in existing work on software metrics are for individual software artifacts,3 e.g., an
individual bug report or an individual piece of code. In this paper, to avoid ambiguous
7. 1.
#13/ 19, 1st Floor, Municipal Colony, Kangayanellore Road, Gandhi Nagar, vellore – 6.
Off: 0416-2247353 / 6066663 Mo: +91 9500218218 /8870603602,
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Website: www.shakastech.com, Email - id: shakastech@gmail.com, info@shakastech.com
denotations, an attribute refers to an extracted feature of a bug data set while a feature refers to a
word of a bug report.
CONCLUSIONS
Bug triage is an expensive step of software maintenance in both labor cost and time cost.
In this paper, we combine feature selection with instance selection to reduce the scale of bug data
sets as well as improve the data quality. To determine the order of applying instance selection
and feature selection for a new bug data set, we extract attributes of each bug data set and train a
predictive model based on historical data sets. We empirically investigate the data reduction for
bug triage in bug repositories of two large open source projects, namely Eclipse and Mozilla.
Our work provides an approach to leveraging techniques on data processing to form reduced and
high-quality bug data in software development and maintenance.