This document describes a study that uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict software defects early in the development process. The study uses metrics data from NASA software projects to train and test the ANFIS model. The results show that the ANFIS model is able to accurately predict defects, with low root mean square error values for both the training and testing data, indicating the model was able to generalize without overfitting. The study concludes ANFIS is an effective technique for software defect prediction that can help improve quality and reduce costs.
STATISTICAL ANALYSIS FOR PERFORMANCE COMPARISONijseajournal
Performance responsiveness and scalability is a make-or-break quality for software. Nearly everyone runs into performance problems at one time or another. This paper discusses about performance issues faced during Pre Examination Process Automation System (PEPAS) implemented in java technology. The challenges faced during the life cycle of the project and the mitigation actions performed. It compares 3 java technologies and shows how improvements are made through statistical analysis in response time of the application. The paper concludes with result analysis.
THE UNIFIED APPROACH FOR ORGANIZATIONAL NETWORK VULNERABILITY ASSESSMENTijseajournal
The present business network infrastructure is quickly varying with latest servers, services, connections,
and ports added often, at times day by day, and with a uncontrollably inflow of laptops, storage media and
wireless networks. With the increasing amount of vulnerabilities and exploits coupled with the recurrent
evolution of IT infrastructure, organizations at present require more numerous vulnerability assessments.
In this paper new approach the Unified process for Network vulnerability Assessments hereafter called as
a unified NVA is proposed for network vulnerability assessment derived from Unified Software
Development Process or Unified Process, it is a popular iterative and incremental software development
process framework.
Prioritizing Test Cases for Regression Testing A Model Based ApproachIJTET Journal
Abstract— Testing is an important phase of quality control of Software Development Life Cycle (SDLC). There are various types of testing methodologies involved to test the application. Regression Testing is a type of testing, which is done to ensure whether the modified features or bug fix had an impact over the existing functionality. Defects are identified by executing the set of test cases. Regression Test case selection is not at all possible to conclude how much retesting is required to identify the deviation when the test suites are larger in size. Prioritization of test cases is done to change the order of test case execution based on the severity. In the proposed a model based approach prioritization of test cases are generated based on UML diagrams (Sequence and State Chart). The modified features have the reflection in the model generation and the number of states and transitions covered. Prioritized test cases are then clustered based upon the severities using dendragram approach. It leads to decrease in the time and cost of regression testing.
Harnessing deep learning algorithms to predict software refactoringTELKOMNIKA JOURNAL
During software maintenance, software systems need to be modified by adding or modifying source code. These changes are required to fix errors or adopt new requirements raised by stakeholders or market place. Identifying thetargeted piece of code for refactoring purposes is considered a real challenge for software developers. The whole process of refactoring mainly relies on software developers’ skills and intuition. In this paper, a deep learning algorithm is used to develop a refactoring prediction model for highlighting the classes that require refactoring. More specifically, the gated recurrent unit algorithm is used with proposed pre-processing steps for refactoring predictionat the class level. The effectiveness of the proposed model is evaluated usinga very common dataset of 7 open source java projects. The experiments are conducted before and after balancing the dataset to investigate the influence of data sampling on the performance of the prediction model. The experimental analysis reveals a promising result in the field of code refactoring prediction
STATISTICAL ANALYSIS FOR PERFORMANCE COMPARISONijseajournal
Performance responsiveness and scalability is a make-or-break quality for software. Nearly everyone runs into performance problems at one time or another. This paper discusses about performance issues faced during Pre Examination Process Automation System (PEPAS) implemented in java technology. The challenges faced during the life cycle of the project and the mitigation actions performed. It compares 3 java technologies and shows how improvements are made through statistical analysis in response time of the application. The paper concludes with result analysis.
THE UNIFIED APPROACH FOR ORGANIZATIONAL NETWORK VULNERABILITY ASSESSMENTijseajournal
The present business network infrastructure is quickly varying with latest servers, services, connections,
and ports added often, at times day by day, and with a uncontrollably inflow of laptops, storage media and
wireless networks. With the increasing amount of vulnerabilities and exploits coupled with the recurrent
evolution of IT infrastructure, organizations at present require more numerous vulnerability assessments.
In this paper new approach the Unified process for Network vulnerability Assessments hereafter called as
a unified NVA is proposed for network vulnerability assessment derived from Unified Software
Development Process or Unified Process, it is a popular iterative and incremental software development
process framework.
Prioritizing Test Cases for Regression Testing A Model Based ApproachIJTET Journal
Abstract— Testing is an important phase of quality control of Software Development Life Cycle (SDLC). There are various types of testing methodologies involved to test the application. Regression Testing is a type of testing, which is done to ensure whether the modified features or bug fix had an impact over the existing functionality. Defects are identified by executing the set of test cases. Regression Test case selection is not at all possible to conclude how much retesting is required to identify the deviation when the test suites are larger in size. Prioritization of test cases is done to change the order of test case execution based on the severity. In the proposed a model based approach prioritization of test cases are generated based on UML diagrams (Sequence and State Chart). The modified features have the reflection in the model generation and the number of states and transitions covered. Prioritized test cases are then clustered based upon the severities using dendragram approach. It leads to decrease in the time and cost of regression testing.
Harnessing deep learning algorithms to predict software refactoringTELKOMNIKA JOURNAL
During software maintenance, software systems need to be modified by adding or modifying source code. These changes are required to fix errors or adopt new requirements raised by stakeholders or market place. Identifying thetargeted piece of code for refactoring purposes is considered a real challenge for software developers. The whole process of refactoring mainly relies on software developers’ skills and intuition. In this paper, a deep learning algorithm is used to develop a refactoring prediction model for highlighting the classes that require refactoring. More specifically, the gated recurrent unit algorithm is used with proposed pre-processing steps for refactoring predictionat the class level. The effectiveness of the proposed model is evaluated usinga very common dataset of 7 open source java projects. The experiments are conducted before and after balancing the dataset to investigate the influence of data sampling on the performance of the prediction model. The experimental analysis reveals a promising result in the field of code refactoring prediction
Software Reliability Testing Training Crash Course - Tonex TrainingBryan Len
Length: 4 Days
Software reliability testing training course prepares you with the most updated knowledge in testing domain allowing you to grow in your job and career, while helping your organization to make profit and excel.
Learn about:
Fundamentals of software testing
Verification & validation methodology
Various software testing techniques
Test elements usage (rule/scenario/case)
Software test management
Different levels of software testing
General testing principles
Test planning
Static analysis techniques
Test design techniques
Using a risk-based approach to testing
Managing the testing process
Managing a test team
Combining tools and automation to support software testing
Risk analysis methods
Software reliability
Software testing terminology
Levels of software testing
Software Testing techniques
Black Box methods and more.
Course Outline:
Overview
Factors Affecting Software Reliability
Software Reliability Models
Data Required for Models
Software Reliability Prediction Models
Software Reliability Evaluation Models
Software Reliability Metrics
Software Fault Trees
Software FMEAs
System Reliability Software Redundancy
Improving Software Reliability
Managing Software Reliability
How Testing Can Cut Effort & Time
How to Plan Effective Testing?
Master Testing Plan
Detailed Test Planning
White Box (Structural) Testing
Integration/System/Special Test Planning
Maintenance and Regression Testing
Automated Testing Tools
Measuring and Managing Testing
Request for more information. Visit tonex.com for course and workshop detail.
Software Reliability Testing Training Crash Course
https://www.tonex.com/training-courses/software-reliability-testing-training-crash-course/
A DECISION SUPPORT SYSTEM FOR ESTIMATING COST OF SOFTWARE PROJECTS USING A HY...ijfcstjournal
One of the major challenges for software, nowadays, is software cost estimation. It refers to estimating the
cost of all activities including software development, design, supervision, maintenance and so on. Accurate
cost-estimation of software projects optimizes the internal and external processes, staff works, efforts and
the overheads to be coordinated with one another. In the management software projects, estimation must
be taken into account so that reduces costs, timing and possible risks to avoid project failure. In this paper,
a decision- support system using a combination of multi-layer artificial neural network and decision tree is
proposed to estimate the cost of software projects. In the model included into the proposed system,
normalizing factors, which is vital in evaluating efforts and costs estimation, is carried out using C4.5
decision tree. Moreover, testing and training factors are done by multi-layer artificial neural network and
the most optimal values are allocated to them. The experimental results and evaluations on Dataset
NASA60 show that the proposed system has less amount of the total average relative error compared with
COCOMO model.
Software Testing and Quality Assurance Assignment 2Gurpreet singh
Short questions :
Q1: What is stress testing?
Q2: What is Cyclomatic complexity?
Q3: Define Object Oriented Testing
Q4: What is regression testing? When it is done?
Q5: How loop testing is different from the path testing?
Q6: What is client server environment?
Q7: What is graph based testing?
Q8: How security testing is useful in real applications?
Q9: What are main characteristics of real time system?
Q10: What are the benefits of data flow testing?
Long Questions:
Q1: Design test case for: ERP, Traffic controller and university management system?
Q2: Assuming a real time system of your choice, discuss the concepts. Analysis and design factors of same, elaborate
Q3: How testing in multiplatform environment is performed?
Q4: Explain graph based testing in detail
Q5: Differentiate between Equivalence partitioning and boundary value analysis
FACTORS ON SOFTWARE EFFORT ESTIMATION ijseajournal
Software effort estimation is an important process of system development life cycle, as it may affect the
success of software projects if project designers estimate the projects inaccurately. In the past of few
decades, various effort prediction models have been proposed by academicians and practitioners.
Traditional estimation techniques include Lines of Codes (LOC), Function Point Analysis (FPA) method
and Mark II Function Points (Mark II FP) which have proven unsatisfactory for predicting effort of all
types of software. In this study, the author proposed a regression model to predict the effort required to
design small and medium scale application software. To develop such a model, the author used 60
completed software projects developed by a software company in Macau. From the projects, the author
extracted factors and applied them to a regression model. A prediction of software effort with accuracy of
MMRE = 8% was constructed.
Program analysis is useful for debugging, testing and maintenance of software systems due to information
about the structure and relationship of the program’s modules . In general, program analysis is performed
either based on control flow graph or dependence graph. However, in the case of aspect-oriented
programming (AOP), control flow graph (CFG) or dependence graph (DG) are not enough to model the
properties of Aspect-oriented (AO) programs. With respect to AO programs, although AOP is good for
modular representation and crosscutting concern, suitable model for program analysis is required to
gather information on its structure for the purpose of minimizing maintenance effort. In this paper Aspect
Oriented Dependence Flow Graph (AODFG) as an intermediate representation model is proposed to
represent the structure of aspect-oriented programs. AODFG is formed by merging the CFG and DG, thus
more information about dependencies between the join points, advice, aspects and their associated
construct with the flow of control from one statement to another are gathered. We discussthe performance
of AODFG by analysing some examples of AspectJ program taken from AspectJ Development Tools
(AJDT).
Using Data Mining to Identify COSMIC Function Point Measurement Competence IJECEIAES
Cosmic Function Point (CFP) measurement errors leads budget, schedule and quality problems in software projects. Therefore, it’s important to identify and plan requirements engineers’ CFP training need quickly and correctly. The purpose of this paper is to identify software requirements engineers’ COSMIC Function Point measurement competence development need by using machine learning algorithms and requirements artifacts created by engineers. Used artifacts have been provided by a large service and technology company ecosystem in Telco. First, feature set has been extracted from the requirements model at hand. To do the data preparation for educational data mining, requirements and COSMIC Function Point (CFP) audit documents have been converted into CFP data set based on the designed feature set. This data set has been used to train and test the machine learning models by designing two different experiment settings to reach statistically significant results. Ten different machine learning algorithms have been used. Finally, algorithm performances have been compared with a baseline and each other to find the best performing models on this data set. In conclusion, REPTree, OneR, and Support Vector Machines (SVM) with Sequential Minimal Optimization (SMO) algorithms achieved top performance in forecasting requirements engineers’ CFP training need.
A Review on Software Fault Detection and Prevention Mechanism in Software Dev...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Software Reliability Testing Training Crash Course - Tonex TrainingBryan Len
Length: 4 Days
Software reliability testing training course prepares you with the most updated knowledge in testing domain allowing you to grow in your job and career, while helping your organization to make profit and excel.
Learn about:
Fundamentals of software testing
Verification & validation methodology
Various software testing techniques
Test elements usage (rule/scenario/case)
Software test management
Different levels of software testing
General testing principles
Test planning
Static analysis techniques
Test design techniques
Using a risk-based approach to testing
Managing the testing process
Managing a test team
Combining tools and automation to support software testing
Risk analysis methods
Software reliability
Software testing terminology
Levels of software testing
Software Testing techniques
Black Box methods and more.
Course Outline:
Overview
Factors Affecting Software Reliability
Software Reliability Models
Data Required for Models
Software Reliability Prediction Models
Software Reliability Evaluation Models
Software Reliability Metrics
Software Fault Trees
Software FMEAs
System Reliability Software Redundancy
Improving Software Reliability
Managing Software Reliability
How Testing Can Cut Effort & Time
How to Plan Effective Testing?
Master Testing Plan
Detailed Test Planning
White Box (Structural) Testing
Integration/System/Special Test Planning
Maintenance and Regression Testing
Automated Testing Tools
Measuring and Managing Testing
Request for more information. Visit tonex.com for course and workshop detail.
Software Reliability Testing Training Crash Course
https://www.tonex.com/training-courses/software-reliability-testing-training-crash-course/
A DECISION SUPPORT SYSTEM FOR ESTIMATING COST OF SOFTWARE PROJECTS USING A HY...ijfcstjournal
One of the major challenges for software, nowadays, is software cost estimation. It refers to estimating the
cost of all activities including software development, design, supervision, maintenance and so on. Accurate
cost-estimation of software projects optimizes the internal and external processes, staff works, efforts and
the overheads to be coordinated with one another. In the management software projects, estimation must
be taken into account so that reduces costs, timing and possible risks to avoid project failure. In this paper,
a decision- support system using a combination of multi-layer artificial neural network and decision tree is
proposed to estimate the cost of software projects. In the model included into the proposed system,
normalizing factors, which is vital in evaluating efforts and costs estimation, is carried out using C4.5
decision tree. Moreover, testing and training factors are done by multi-layer artificial neural network and
the most optimal values are allocated to them. The experimental results and evaluations on Dataset
NASA60 show that the proposed system has less amount of the total average relative error compared with
COCOMO model.
Software Testing and Quality Assurance Assignment 2Gurpreet singh
Short questions :
Q1: What is stress testing?
Q2: What is Cyclomatic complexity?
Q3: Define Object Oriented Testing
Q4: What is regression testing? When it is done?
Q5: How loop testing is different from the path testing?
Q6: What is client server environment?
Q7: What is graph based testing?
Q8: How security testing is useful in real applications?
Q9: What are main characteristics of real time system?
Q10: What are the benefits of data flow testing?
Long Questions:
Q1: Design test case for: ERP, Traffic controller and university management system?
Q2: Assuming a real time system of your choice, discuss the concepts. Analysis and design factors of same, elaborate
Q3: How testing in multiplatform environment is performed?
Q4: Explain graph based testing in detail
Q5: Differentiate between Equivalence partitioning and boundary value analysis
FACTORS ON SOFTWARE EFFORT ESTIMATION ijseajournal
Software effort estimation is an important process of system development life cycle, as it may affect the
success of software projects if project designers estimate the projects inaccurately. In the past of few
decades, various effort prediction models have been proposed by academicians and practitioners.
Traditional estimation techniques include Lines of Codes (LOC), Function Point Analysis (FPA) method
and Mark II Function Points (Mark II FP) which have proven unsatisfactory for predicting effort of all
types of software. In this study, the author proposed a regression model to predict the effort required to
design small and medium scale application software. To develop such a model, the author used 60
completed software projects developed by a software company in Macau. From the projects, the author
extracted factors and applied them to a regression model. A prediction of software effort with accuracy of
MMRE = 8% was constructed.
Program analysis is useful for debugging, testing and maintenance of software systems due to information
about the structure and relationship of the program’s modules . In general, program analysis is performed
either based on control flow graph or dependence graph. However, in the case of aspect-oriented
programming (AOP), control flow graph (CFG) or dependence graph (DG) are not enough to model the
properties of Aspect-oriented (AO) programs. With respect to AO programs, although AOP is good for
modular representation and crosscutting concern, suitable model for program analysis is required to
gather information on its structure for the purpose of minimizing maintenance effort. In this paper Aspect
Oriented Dependence Flow Graph (AODFG) as an intermediate representation model is proposed to
represent the structure of aspect-oriented programs. AODFG is formed by merging the CFG and DG, thus
more information about dependencies between the join points, advice, aspects and their associated
construct with the flow of control from one statement to another are gathered. We discussthe performance
of AODFG by analysing some examples of AspectJ program taken from AspectJ Development Tools
(AJDT).
Using Data Mining to Identify COSMIC Function Point Measurement Competence IJECEIAES
Cosmic Function Point (CFP) measurement errors leads budget, schedule and quality problems in software projects. Therefore, it’s important to identify and plan requirements engineers’ CFP training need quickly and correctly. The purpose of this paper is to identify software requirements engineers’ COSMIC Function Point measurement competence development need by using machine learning algorithms and requirements artifacts created by engineers. Used artifacts have been provided by a large service and technology company ecosystem in Telco. First, feature set has been extracted from the requirements model at hand. To do the data preparation for educational data mining, requirements and COSMIC Function Point (CFP) audit documents have been converted into CFP data set based on the designed feature set. This data set has been used to train and test the machine learning models by designing two different experiment settings to reach statistically significant results. Ten different machine learning algorithms have been used. Finally, algorithm performances have been compared with a baseline and each other to find the best performing models on this data set. In conclusion, REPTree, OneR, and Support Vector Machines (SVM) with Sequential Minimal Optimization (SMO) algorithms achieved top performance in forecasting requirements engineers’ CFP training need.
A Review on Software Fault Detection and Prevention Mechanism in Software Dev...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A new model for software costestimationijfcstjournal
Accurate and realistic estimation is always considered to be a great challenge in software industry.
Software Cost Estimation (SCE) is the standard application used to manage software projects. Determining
the amount of estimation in the initial stages of the project depends on planning other activities of the
project. In fact, the estimation is confronted with a number of uncertainties and barriers’, yet assessing the
previous projects is essential to solve this problem. Several models have been developed for the analysis of
software projects. But the classical reference method is the COCOMO model, there are other methods
which are also applied such as Function Point (FP), Line of Code(LOC); meanwhile, the expert`s opinions
matter in this regard. In recent years, the growth and the combination of meta-heuristic algorithms with
high accuracy have brought about a great achievement in software engineering. Meta-heuristic algorithms
which can analyze data from multiple dimensions and identify the optimum solution between them are
analytical tools for the analysis of data. In this paper, we have used the Harmony Search (HS)algorithm for
SCE. The proposed model which is a collection of 60 standard projects from Dataset NASA60 has been
assessed.The experimental results show that HS algorithm is a good way for determining the weight
similarity measures factors of software effort, and reducing the error of MRE.
A NEW MODEL FOR SOFTWARE COSTESTIMATION USING HARMONY SEARCHijfcstjournal
Accurate and realistic estimation is always considered to be a great challenge in software industry.
Software Cost Estimation (SCE) is the standard application used to manage software projects. Determining
the amount of estimation in the initial stages of the project depends on planning other activities of the
project. In fact, the estimation is confronted with a number of uncertainties and barriers’, yet assessing the
previous projects is essential to solve this problem. Several models have been developed for the analysis of
software projects. But the classical reference method is the COCOMO model, there are other methods
which are also applied such as Function Point (FP), Line of Code(LOC); meanwhile, the expert`s opinions
matter in this regard. In recent years, the growth and the combination of meta-heuristic algorithms with
high accuracy have brought about a great achievement in software engineering. Meta-heuristic algorithms
which can analyze data from multiple dimensions and identify the optimum solution between them are
analytical tools for the analysis of data. In this paper, we have used the Harmony Search (HS)algorithm for
SCE. The proposed model which is a collection of 60 standard projects from Dataset NASA60 has been
assessed.The experimental results show that HS algorithm is a good way for determining the weight
similarity measures factors of software effort, and reducing the error of MRE.
A survey of predicting software reliability using machine learning methodsIAESIJAI
In light of technical and technological progress, software has become an urgent need in every aspect of human life, including the medicine sector and industrial control. Therefore, it is imperative that the software always works flawlessly. The information technology sector has witnessed a rapid expansion in recent years, as software companies can no longer rely only on cost advantages to stay competitive in the market, but programmers must provide reliable and high-quality software, and in order to estimate and predict software reliability using machine learning and deep learning, it was introduced A brief overview of the important scientific contributions to the subject of software reliability, and the researchers' findings of highly efficient methods and techniques for predicting software reliability.
A hybrid fuzzy ann approach for software effort estimationijfcstjournal
Software development effort estimation is one of the major activities in software project management.
During the project proposal stage there is high probability of estimates being made inaccurate but later on
this inaccuracy decreases. In the field of software development there are certain matrices, based on which
the effort estimation is being made. Till date various methods has been proposed for software effort
estimation, of which the non algorithmic methods, like artificial intelligence techniques have been very
successful. A Hybrid Fuzzy-ANN model, known as Adaptive Neuro Fuzzy Inference System (ANFIS) is more
suitable in such situations. The present paper is concerned with developing software effort estimation
model based on ANFIS. The present study evaluates the efficiency of the proposed ANFIS model, for which
COCOMO81 datasets has been used. The result so obtained has been compared with Artificial Neural
Network (ANN) and Intermediate COCOCMO model developed by Boehm. The results were analyzed using
Magnitude of Relative Error (MRE) and Root Mean Square Error (RMSE). It is observed that the ANFIS
provided better results than ANN and COCOMO model.
Review on Algorithmic and Non Algorithmic Software Cost Estimation Techniquesijtsrd
Effective software cost estimation is the most challenging and important activities in software development. Developers want a simple and accurate method of efforts estimation. Estimation of the cost before starting of work is a prediction and prediction always not accurate. Software effort estimation is a very critical task in the software engineering and to control quality and efficiency a suitable estimation technique is crucial. This paper gives a review of various available software effort estimation methods, mainly focus on the algorithmic model and non algorithmic model. These existing methods for software cost estimation are illustrated and their aspect will be discussed. No single technique is best for all situations, and thus a careful comparison of the results of several approaches is most likely to produce realistic estimation. This paper provides a detailed overview of existing software cost estimation models and techniques. This paper presents the strength and weakness of various cost estimation methods. This paper focuses on some of the relevant reasons that cause inaccurate estimation. Pa Pa Win | War War Myint | Hlaing Phyu Phyu Mon | Seint Wint Thu "Review on Algorithmic and Non-Algorithmic Software Cost Estimation Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26511.pdfPaper URL: https://www.ijtsrd.com/engineering/-/26511/review-on-algorithmic-and-non-algorithmic-software-cost-estimation-techniques/pa-pa-win
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Software metricsIntroduction
Attributes of Software Metrics
Activities of a Measurement Process
Types
Normalization of Metrics
Help software engineers to gain insight into the design and construction of the software
Activities of a Measurement Process
To answer this we need to know the size & complexity of the projects.
But if we normalize the measures, it is possible to compare the two
For normalization we have 2 ways-
Size-Oriented Metrics
Function Oriented Metrics
A Complexity Based Regression Test Selection StrategyCSEIJJournal
Software is unequivocally the foremost and indispensable entity in this technologically driven world.
Therefore quality assurance, and in particular, software testing is a crucial step in the software
development cycle. This paper presents an effective test selection strategy that uses a Spectrum of
Complexity Metrics (SCM). Our aim in this paper is to increase the efficiency of the testing process by
significantly reducing the number of test cases without having a significant drop in test effectiveness. The
strategy makes use of a comprehensive taxonomy of complexity metrics based on the product level (class,
method, statement) and its characteristics.We use a series of experiments based on three applications with
a significant number of mutants to demonstrate the effectiveness of our selection strategy.For further
evaluation, we compareour approach to boundary value analysis. The results show the capability of our
approach to detect mutants as well as the seeded errors.
COMPARATIVE STUDY OF SOFTWARE ESTIMATION TECHNIQUES ijseajournal
Many information technology firms among other organizations have been working on how to perform estimation of the sources such as fund and other resources during software development processes. Software development life cycles require lot of activities and skills to avoid risks and the best software estimation technique is supposed to be employed. Therefore, in this research, a comparative study was conducted, that consider the accuracy, usage, and suitability of existing methods. It will be suitable for the project managers and project consultants during the whole software project development process. In this project technique such as linear regression; both algorithmic and non-algorithmic are applied. Model, composite and regression techniques are used to derive COCOMO, COCOMO II, SLIM and linear multiple respectively. Moreover, expertise-based and linear-based rules are applied in non-algorithm methods. However, the technique needs some advancement to reduce the errors that are experienced during the software development process. Therefore, this paper in relation to software estimation techniques has proposed a model that can be helpful to the information technology firms, researchers and other firms that use information technology in the processes such as budgeting and decision-making processes.
Similar to IRJET- A Novel Approach on Computation Intelligence Technique for Software Defect Prediction during Early Stage of Software Development (20)
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Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
TOP 10 B TECH COLLEGES IN JAIPUR 2024.pptxnikitacareer3
Looking for the best engineering colleges in Jaipur for 2024?
Check out our list of the top 10 B.Tech colleges to help you make the right choice for your future career!
1) MNIT
2) MANIPAL UNIV
3) LNMIIT
4) NIMS UNIV
5) JECRC
6) VIVEKANANDA GLOBAL UNIV
7) BIT JAIPUR
8) APEX UNIV
9) AMITY UNIV.
10) JNU
TO KNOW MORE ABOUT COLLEGES, FEES AND PLACEMENT, WATCH THE FULL VIDEO GIVEN BELOW ON "TOP 10 B TECH COLLEGES IN JAIPUR"
https://www.youtube.com/watch?v=vSNje0MBh7g
VISIT CAREER MANTRA PORTAL TO KNOW MORE ABOUT COLLEGES/UNIVERSITITES in Jaipur:
https://careermantra.net/colleges/3378/Jaipur/b-tech
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