Tracing Requirements as a Problem of Machine Learning
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Software requirement engineering and evolution essential to software development process, which defines and elaborates what is to be built in a project. Requirements are mostly written in text and will later evolve to fine-grained and actionable artifacts with details about system configurations, technology stacks, etc. Tracing the evolution of requirements enables stakeholders to determine the origin of each requirement and understand how well the software’s design reflects to its requirements. Reckoning requirements traceability is not a trivial task, a machine learning approach is used to classify traceability between various associated requirements. In particular, a 2-learner, ontology-based, pseudo-instancesenhanced approach, where two classifiers are trained to separately exploit two types of features, lexical features and features derived from a hand-built ontology, is investigated for such task. The hand-built ontology is also leveraged to generate pseudo training instances to improve machine learning results
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pseudo training instances to improve machine learning results. In comparison to a supervised baseline system that uses only lexical features, our approach yields a relative error reduction of 56.0%. Most interestingly, results do not deteriorate when the hand-built ontology is replaced with its automatically
constructed counterpart.
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Agent oriented software engineering is a new
emerging technique which is growing very
rapidly. Software development industries have
invested huge efforts in this domain and results
published by many of them are very exiting [1].
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makes it possible for the designers to visualize
in terms of real life problem solving scenarios
where socio-logical [2] characteristics of agents
automatically activate the timely checks for any
problem in domain and to solve the same using
agents.
Agents are very helpful in the software
development life cycle. Experiments carried out
in past have shown [2][9][10] the improvement
in the SDLC and conclusion is that agents can be
very helpful in cost and effort minimization; if
tuned properly. Fine-tuning of agents and SDLC
process-state-plug-in for two-way
communications results in agent based software
development process where intelligent agents
will take decisions for better time and resource
utilization.
Fine-tuning of agents and SDLC process-state-
plug-in for two-way communications results in
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where intelligent agents will take decisions for
better time and resource utilization. Agents are
capable of storing historic data, which helps in
decision-making using heuristic based approach.
This paper discusses the details of one such
experiment conducted to improve the
requirement analysis process with the help of
proactive agents. Agents automatically sense the
requirement environment and propose their own
set of important requirement checklist. This is
sort of intelligent assistance with domain
heuristic, which leads to cover all possible
requirement entities of the problem domain.
2. RELATED WORK
Michael Wooldridge, Nicholas R. Jennings &
David Kinny describe the analysis process using
agent-oriented approach [1]. They have
considered the GAIA notations. The analysis
stages of Gaia are:
1) Identify the agent’s roles in the system, which
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prioritizing requirement attributes using rank
based Agents.
Ashok Kumar Vinay Goyal
Professor Assistant Professor
Department of Computer Science and Applications Department of MCA
Kurukshetra University, Kurukshetra, India Panipat Institute of Engineering & Technology
Panipat, India
Abstract- This paper proposes a new technique in the
domain of Agent oriented software engineering. Agents
work in autonomous environments and can respond to
agent triggers. Agents can be very useful in requirement
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they can react towards the requirement triggers and
result in aligned notations to identify the best possible
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emerging technique which is growing very
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published by many of them are very exiting [1].
The autonomous and reactive nature of agents
makes it possible for the designers to visualize
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automatically activate the timely checks for any
problem in domain and to solve the same using
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development life cycle. Experiments carried out
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in the SDLC and conclusion is that agents can be
very helpful in cost and effort minimization; if
tuned properly. Fine-tuning of agents and SDLC
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development process where intelligent agents
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capable of storing historic data, which helps in
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proactive agents. Agents automatically sense the
requirement environment and propose their own
set of important requirement checklist. This is
sort of intelligent assistance with domain
heuristic, which leads to cover all possible
requirement entities of the problem domain.
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David Kinny describe the analysis process using
agent-oriented approach [1]. They have
considered the GAIA notations. The analysis
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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.
ONE HIDDEN LAYER ANFIS MODEL FOR OOS DEVELOPMENT EFFORT ESTIMATION
ONE HIDDEN LAYER ANFIS MODEL FOR OOS DEVELOPMENT EFFORT ESTIMATIONInternational Journal of Technical Research & Application
In the present paper, applicability and
capability of A.I techniques for effort estimation prediction has
been investigated. It is seen that neuro fuzzy models are very
robust, characterized by fast computation, capable of handling
the distorted data. Due to the presence of data non-linearity, it is
an efficient quantitative tool to predict effort estimation. The one
hidden layer network has been developed named as OHLANFIS
using MATLAB simulation environment.
Here the initial parameters of the OHLANFIS are
identified using the subtractive clustering method. Parameters of
the Gaussian membership function are optimally determined
using the hybrid learning algorithm. From the analysis it is seen
that the Effort Estimation prediction model developed using
OHLANFIS technique has been able to perform well over normal
ANFIS Model. A survey of predicting software reliability using machine learning methods
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 Defect Prediction Model for Software Product based on ANFIS
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
A Defect Prediction Model for Software Product based on ANFIS
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
Study of Software Defect Prediction using Forward Pass RNN with Hyperbolic Ta...
For the IT sector and software specialists, software failure prediction and proneness have long been seen as crucial issues. Conventional methods need prior knowledge of errors or malfunctioning modules in order to identify software flaws inside an application. By using machine learning approaches, automated software fault recovery models allow the program to substantially forecast and recover from software problems. This feature helps the program operate more efficiently and lowers errors, time, and expense. Using machine learning methods, a software fault prediction development model was presented, which might allow the program to continue working on its intended mission. Additionally, we assessed the models performance using a variety of optimization assessment benchmarks, including accuracy, f1 measure, precision, recall, and specificity. Convolutional neural networks and its hyperbolic tangent functions are the basis of the deep learning prediction model FPRNN HTF Forward Pass RNN with Hyperbolic Tangent Function technique. The assessment procedure demonstrated the high accuracy rate and effective application of CNN algorithms. Moreover, a comparative measure is used to evaluate the suggested prediction model against other methodologies. The gathered data demonstrated the superior performance of the FPRNN HTF technique. Swati Rai | Dr. Kirti Jain "Study of Software Defect Prediction using Forward Pass RNN with Hyperbolic Tangent Function" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-6 , December 2023, URL: https://www.ijtsrd.com/papers/ijtsrd60159.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/60159/study-of-software-defect-prediction-using-forward-pass-rnn-with-hyperbolic-tangent-function/swati-rai
A DECISION SUPPORT SYSTEM FOR ESTIMATING COST OF SOFTWARE PROJECTS USING A HY...
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.
A DECISION SUPPORT SYSTEM FOR ESTIMATING COST OF SOFTWARE PROJECTS USING A HY...
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.
Review on Agile Method with Text Mining
Working software measures the progress. Basically, Agile method involves interleaving the specification, implementation, design and testing. Series of versions are developed with the involvement of and evaluation by the stake holders in each version. Agile methods aim at reducing the software process overheads (like documentation) and concentrate more on code rather than the design. Customer involvement, incremental delivery, freedom of developers to evolve new working methods, change management, and last but not the least simplicity is the basic essence of Agile development. Agile methodologies are well suited for small as well as medium sized projects.
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Tracing Requirements as a Problem of Machine Learning
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Tracing Requirements as a Problem of Machine Learning
- 1. International Journal of Software Engineering & Applications (IJSEA)-ERA Indexed ISSN : 0975 - 9018 ( Online ); 0976-2221 ( Print ) http://www.airccse.org/journal/ijsea/ijsea.html
- 2. Tracing Requirements as a Problem of Machine Learning Zeheng Li and LiGuo Huang AIRCC Publishing Corporation
- 3. AIRCC Publishing Corporation Abstract Software requirement engineering and evolution essential to software development process, which defines and elaborates what is to be built in a project. Requirements are mostly written in text and will later evolve to fine-grained and actionable artifacts with details about system configurations, technology stacks, etc. Tracing the evolution of requirements enables stakeholders to determine the origin of each requirement and understand how well the software’s design reflects to its requirements. Reckoning requirements traceability is not a trivial task, a machine learning approach is used to classify traceability between various associated requirements. In particular, a 2-learner, ontology-based, pseudo-instancesenhanced approach, where two classifiers are trained to separately exploit two types of features, lexical features and features derived from a hand-built ontology, is investigated for such task. The hand-built ontology is also leveraged to generate pseudo training instances to improve machine learning results
- 4. AIRCC Publishing Corporation keywords Requirements Traceability, Software Design, Machine Learning
- 7. AIRCC Publishing Corporation CONCLUSION A 2-learner, ontology-based, pseudo-instances-enhanced approach to supervised traceability pre- diction has been investigated. Results showed that (1) our approach is effective: in comparison to the best baseline, relative error reduces by 56.0%; (2) the pseudo instances extension is effective, which is able to mitigate the situation when human labelled links are insufficient; and (3) also interestingly, results obtained via induced clusters were as competitive as those obtained via man- ual clusters, which indicates potential to automate building of ontology rationales for traceability prediction and reduce human effort. .
- 8. AIRCC Publishing Corporation For More Details Please Visit: http://aircconline.com/ijsea/V9N4/9418ijsea02.pdf