machine learning and software risk in sdlc


Risk management is an essential part for high quality software development processes. More probably Risks are events that could adversely affect the development of the projects or organization environment. Risk or Risk factor can damage critical factors such as budget, time or resources.

Overview of software defect prediction using machine learning algorithms
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Overview of Software Defect Prediction using Machine Learning Algorithms The major risk factors related with a software defect which is not detected during the early phase any organization is to determine and correct the defects in an early phase of Software Development Life

Neuro-fuzzy based software risk estimation tool
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With the use of hybrid approach ie Neural network and support vector machine , Intrusion detection system is constructed Intrusion detection using neural networks and support vector machines Hinfra: Hierarchical neuro-fuzzy learning for online risk assessment

Machine Learning : A Software Process Reengineering in Software Development Organization
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Resources include people, machines , and raw materials The use of Machine Learning is increasing in digital business processes. In 201 Nadana and Iyakutti proposed an ML based risk control method BSSP (Bayesian Statistics Software Process) and obtained 29

Natural Language Processing to Quantify Security Effort in the Software Development Lifecycle.
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of an initial study investigating the potential of applying NLP and machine learning techniques to broader number of data sets, spanning a wider range of software project types teams, (2) more sophisticated classification methods such as Support Vector Machines or ensemble

An efficient software development life cycle model for developing software project
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New proposed SDLC Model 1. Projects success is highly dependent on the risk analysis phase He is working as Asssociate Lecturer in department of CSE, BIT Mesra, Patna Campus,. His research area is Machine Learning (CBR), Software Engineering and Networking

Management of Changes in Software Requirements during Development Phases
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Fuzzy-ExCOM Software Project Risk Assessment 2012 11th International Conference on Machine Learning and Applications

Trivial model for mitigation of risks in software development life cycle
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The software risks have been also grouped into process, project and product risks it can be identified that the impact and probability of risk factors is generally calculated by doing the interviews, surveys and learning from personal (c) Continuous risk management (CRM): CRM

A framework for performance prediction of service-oriented architecture
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SOA based applications is regarded one of the complex tasks that face software developers and Finally, machine learning technique used to predict the application model performance. The prediction result Risk means design does not meet customer requirement and No Risk

Evolving a new software development life cycle model ( SDLC ) incorporated with release management
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algorithm, the adaptability of an operating system, or the form that human/ machine interaction should Working on this paper has been a great learning experience for me REFERENCES Software Development Life Cycle ( SDLC ) the five common principles.htm Software

The machine learning in software project management: A journey. Part II
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neural networks, radial basis function networks, MARS, least squares support vector machines . An estimation techniques which are not 2) to discover what is the need of learning ; Page 17. Software project management and machine learning : A journey. Part II 45

Comparing various SDLC models and the new proposed model on the basis of available methodology
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Analyst capability Software engineering capability Applications experience Virtual machine experience Programming which SDLC model is more satisfactory successful for developing specified software Working on this paper has been a great learning experience for

Comparative study of software cost estimation techniques
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data 13 Fuzzy Machine learning model 1957. Galorath, DD, Evans, MW Software sizing, estimation, and risk management: When performance is measured performance improves . Boca Raton, FL: Auerbach,2006. [10

HYBRID SOFTWARE DEVELOPMENT MODEL-PROTOTYPE CENTRIC (PC)
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Here in risk analysis process we opt to machine learning technique called support vector machines in short SVM. The Risk analysis stage of the PC mathematical model called Quantum particle swarm optimization. Fig.1 : Hybrid Software development process model Page 3

Effort estimation methods in software development using machine learning algorithms
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X Unmanaged risks X Poor communication among customers, developers, and users after applying machine learning techniques are compared with each other as well as lines of code, which is obtained from the coding phase of software development life cycle ( SDLC )

A survey on machine learning techniques used for software quality prediction
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Page 9. A survey on machine learning techniques used for software quality prediction 11 2.6.3 Fault prediction using tree-based software quality estimation model In the process of software development, if the high risk modules of software can be identified at an early stage

An Effective Software Effort Estimation based on Functional Points using Soft Computing Techniques
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ME) and number of Inputs, Outputs, Interfaces, Enquiries using mentioned machine learning techniques and uses Fuzzy Logic Model, Neural Networks, Support Vector Machines , Evolutionary Algorithms system and the training is done using neuro-adaptive learning methods as

Security Risks in the Software Development Lifecycle
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1) Writing programs for people rather than for machines wherein: ▪ a program must not require the users to take in too many facts at once ▪ names are meaningful, distinctive, and consistent ▪ the formatting and style Risk management applied to software development projects

Integrating the Internet of Things Into Software Engineering Practices
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Agile Software Coding Standards Collaborative Software Language Specification Machine Learning Risk Assessment Security Assessment Software Architecture System Testing Web-Based Development

AN IMPLEMENTATION OF SOFTWARE EFFORT DURATION AND COST ESTIMATION WITH STATISTICAL AND MACHINE LEARNING APPROACHES
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Support vector machines are used to perform classification and regression analysis by constructing hyper plane or various hyper planes in a high Page 6. An Implementation of Software Effort Duration and Cost Estimation with Statistical and Machine Learning Approaches

Comparative analysis of software cost and effort estimation methods: a review
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Risk Resolution(RESL) Reflects the extent of risk analysis carried out Cuadrado-Gallego, JJ, Rodri, et al. Analogies and Differences between Machine learning and Experts Based Software Project Effort Estimation


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