• Corpus ID: 15316478

Defect Prediction in Software Projects-Using Genetic Algorithm based Fuzzy C-Means Clustering and Random Forest Classifier

  title={Defect Prediction in Software Projects-Using Genetic Algorithm based Fuzzy C-Means Clustering and Random Forest Classifier},
  author={P. Suma and Ramaswamy V},
Software project success is based on prediction of defects at early stages of software development. Aaccurate prediction of defect prone modules in software development process enables effective discovery and identification of the defects. Such prediction approaches are valuable for the large scale systems, where verification experts need to focus their attention and resources to problem areas in the system under development. Identifying and locating defects in software projects to measure the… 

A Review on Artificial intelligence Approach on Prediction of Software Defects

  • Computer Science
  • 2016
This paper surveys literature review of articles for the past many years to explore how various prediction methodologies have been developed during this period in order to take care of the issues related to software defect.

Towards a Machine Learning Model for Predicting Failure of Agile Software Projects

A survey of machine learning approaches for predicting failure of agile software projects is introduced and a proposed machine learning model for predictingFailure of agileSoftware projects is proposed.

Intelligent System for Forecasting Failure of Agile Projects

The researchers propose an approach for revealing the failure of agile software projects based on two intelligent techniques: fuzzy logic and multiple linear regressions (MLR).

A Deep Introduction to AI Based Software Defect Prediction (SDP) and its Current Challenges

Its novel attempt to isolate defective software units enables defect removal as well as better utilization of resources in software development and maintenance activities and there is plenty of scope to strengthen SDP and also take measures to promote its practical use within the industry by simplifying SDP models and quantifying their outputs and benefits.

Feature Selection for Construction Organizational Competencies Impacting Performance

The main objective of the research presented in this paper is the development of a fuzzy inference system (FIS) applying fuzzy c-means clustering and FS using genetic algorithms (GAs).

The Impact of SMOTE and Grid Search on Maintainability Prediction Models

The main focus in this study is to propose the use of Grid search method for tuning hyper-parameters and to balance datasets using SMOTE technique and it is found that balanced data and tuning parameters are suitable in order to obtain the best performance of ML techniques.

Hyper-Parameter Optimization of Classifiers, Using an Artificial Immune Network and Its Application to Software Bug Prediction

A software bug prediction model is proposed which uses machine learning classifiers in conjunction with the Artificial Immune Network (AIN) to improve bug prediction accuracy through its hyper-parameter optimization.

Towards an Efficient Method of Modeling "Next Best Action" for Digital Buyer's Journey in B2B

The paper provides a unique approach to translate the propensity model at an email address level into a segment that can target a group of email addresses and shows that the proposed method outperforms the traditional classification methods.



On the Applicability of Machine Learning Techniques for Object Oriented Software Fault Prediction

The aim of this paper is to find the relation of object oriented metrics and fault proneness of a class and to analyze and compare the predictive accuracy of machine learning classifiers.

Empirical validation of object-oriented metrics on open source software for fault prediction

This paper calculated the object-oriented metrics given by Chidamber and Kemerer to illustrate how fault-proneness detection of the source code of the open source Web and e-mail suite called Mozilla can be carried out and checked the values obtained against the number of bugs found in its bug database to validate the usefulness of these metrics for fault- proneness prediction.

A hierarchical model for object-oriented design quality assessment

This paper represents proposed model for estimation quality of software product, which can forecast the quality of the object oriented system by analyzing the metric data.

Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults

This paper uses logistic regression and machine learning methods to empirically investigate the usefulness of object-oriented design metrics, specifically, a subset of the Chidamber and Kemerer suite, in predicting fault-proneness when taking fault severity into account and indicates that most of these design metrics are statistically related to fault- proneness of classes across fault severity.

A Metrics Suite for Object Oriented Design

This research addresses the needs for software measures in object-orientation design through the development and implementation of a new suite of metrics for OO design, and suggests ways in which managers may use these metrics for process improvement.

Quantitative Analysis of Faults and Failures in a Complex Software System

Strong evidence of a counter-intuitive relationship between pre- and postrelease faults is found; those modules which are the most fault-prone prerelease are among the least fault- prone postrelease, while conversely, the modulesWhich are most Fault-prone postrelease areAmong the least faults discovered in prerelease.

Object-oriented metrics that predict maintainability

Empirical analysis for investigating the effect of object-oriented metrics on fault proneness: a replicated case study

The importance of software measurement is increasing, leading to the development of new measurement techniques. Many metrics have been proposed related to the various object-oriented (OO) constructs

A Validation of Object-oriented Metrics

The results indicate that out of the 24 metrics proposed, only four are actually related to faults after controlling for class size, and that only two of these are useful for the construction of prediction models.