scholarly journals A Probabilistic and Deterministic based Defect Prediction through Defect Association Learning in Software Development

2020 ◽  
Vol 8 (6) ◽  
pp. 4264-4270
Keyword(s):  
Software Development ◽  
Probability Distributions ◽  
Random Variable ◽  
Defect Prediction ◽  
Probability Method ◽  
Association Learning ◽  
Software Defect ◽  
Association Method ◽  
Potential Methods ◽  
Additional Calculation

Software development is a multitasking activity by an individual or group of team. Every one activity engages diverse tasks and complication. To accomplish quality improvement, it is essential to make every activity task free of defects. But locating and correcting defects is more expensive and time-intense. In the past, many potential methods have been used to predict potential drawbacks in the program based on the theory of probability facts. Because the probability method applies a random variable and probability distributions to find a solution, the result is always in a possible range that can be true at some time or may also be wrong. Therefore, an additional calculation method coupled with the probability of making it more accurate and new in predicting the defect of the program. In this paper, we propose a Probabilistic and Deterministic based Defect Prediction (PD-DP) through Defect Association Learning (DAL). The PD-DP implements a Probability association method (PAM) and Deterministic association method (DAM) to predict the software defect accurately in software development. The experimental evaluation of the PP-DP in compare to existing prediction methods shows enhancement in prediction accuracy

scholarly journals Towards Design and Feasibility Analysis of DePaaS: AI Based Global Unified Software Defect Prediction Framework

2022 ◽  
Vol 12 (1) ◽  
pp. 493
Author(s):  
Mahesha Pandit ◽  
Deepali Gupta ◽  
Divya Anand ◽  
Nitin Goyal ◽  
Hani Moaiteq Aljahdali ◽  
...  
Keyword(s):  
Software Development ◽  
Prediction Models ◽  
Easy Access ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Research And Practice ◽  
Software Defect ◽  
Software Modules ◽  
Software Development Teams ◽  
Defect Prediction Models

Using artificial intelligence (AI) based software defect prediction (SDP) techniques in the software development process helps isolate defective software modules, count the number of software defects, and identify risky code changes. However, software development teams are unaware of SDP and do not have easy access to relevant models and techniques. The major reason for this problem seems to be the fragmentation of SDP research and SDP practice. To unify SDP research and practice this article introduces a cloud-based, global, unified AI framework for SDP called DePaaS—Defects Prediction as a Service. The article describes the usage context, use cases and detailed architecture of DePaaS and presents the first response of the industry practitioners to DePaaS. In a first of its kind survey, the article captures practitioner’s belief into SDP and ability of DePaaS to solve some of the known challenges of the field of software defect prediction. This article also provides a novel process for SDP, detailed description of the structure and behaviour of DePaaS architecture components, six best SDP models offered by DePaaS, a description of algorithms that recommend SDP models, feature sets and tunable parameters, and a rich set of challenges to build, use and sustain DePaaS. With the contributions of this article, SDP research and practice could be unified enabling building and using more pragmatic defect prediction models leading to increase in the efficiency of software testing.

scholarly journals Software Defect Prediction for Healthcare Big Data: An Empirical Evaluation of Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Bilal Khan ◽  
Rashid Naseem ◽  
Muhammad Arif Shah ◽  
Karzan Wakil ◽  
Atif Khan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Software Development ◽  
Absolute Error ◽  
Defect Prediction ◽  
Support Vector ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Squared Error ◽  
Average Accuracy

Software defect prediction (SDP) in the initial period of the software development life cycle (SDLC) remains a critical and important assignment. SDP is essentially studied during few last decades as it leads to assure the quality of software systems. The quick forecast of defective or imperfect artifacts in software development may serve the development team to use the existing assets competently and more effectively to provide extraordinary software products in the given or narrow time. Previously, several canvassers have industrialized models for defect prediction utilizing machine learning (ML) and statistical techniques. ML methods are considered as an operative and operational approach to pinpoint the defective modules, in which moving parts through mining concealed patterns amid software metrics (attributes). ML techniques are also utilized by several researchers on healthcare datasets. This study utilizes different ML techniques software defect prediction using seven broadly used datasets. The ML techniques include the multilayer perceptron (MLP), support vector machine (SVM), decision tree (J48), radial basis function (RBF), random forest (RF), hidden Markov model (HMM), credal decision tree (CDT), K-nearest neighbor (KNN), average one dependency estimator (A1DE), and Naïve Bayes (NB). The performance of each technique is evaluated using different measures, for instance, relative absolute error (RAE), mean absolute error (MAE), root mean squared error (RMSE), root relative squared error (RRSE), recall, and accuracy. The inclusive outcome shows the best performance of RF with 88.32% average accuracy and 2.96 rank value, second-best performance is achieved by SVM with 87.99% average accuracy and 3.83 rank values. Moreover, CDT also shows 87.88% average accuracy and 3.62 rank values, placed on the third position. The comprehensive outcomes of research can be utilized as a reference point for new research in the SDP domain, and therefore, any assertion concerning the enhancement in prediction over any new technique or model can be benchmarked and proved.

A Regression Analysis Based Model for Defect Learning and Prediction in Software Development

2021 ◽  
Vol 40 (3) ◽  
pp. 617-629
Author(s):  
Mashooque Ahmed Memon ◽  
Mujeeb-ur-Rehman Maree Baloch ◽  
Muniba Memon ◽  
Syed Hyder Abbas Musavi
Keyword(s):  
Regression Analysis ◽  
Software Development ◽  
Sensitivity And Specificity ◽  
Experimental Evaluation ◽  
Prediction Accuracy ◽  
Defect Prediction ◽  
Development Effort ◽  
Association Learning ◽  
The Past ◽  
Project Data

The development of software undergoes multiple regression phases to deliver quality software. Therefore, to minimize the development effort, time and cost it is very important to understand the probable defects associated with the designed modules. It is possible that occurrence of a range of defects may impact the designed modules which need to be predicted in advance to have a close inter-association with the depended modules. Most of the existing defect prediction classifier mechanisms are derived from the past project data learning, but it is not sufficient for new project defect predicting as the new design may have a different kind of parameters and constraints. This paper recommends Regression Analysis (RA) based defect learning and prediction Defect Prediction (RA-DP) mechanism to support the defective or non-defective prediction for quality software development. The RA-DP approach provides two methods to perform this prediction analysis. It initially presents an association learning through RA to construct the regression rules from the learned knowledge required for the defect prediction. The constructed regression rules are used for defect prediction and analysis. To measure the performance of the RA-DP a regression experimental evaluation is performed over the defect-prone PROMISE dataset from NASA project. The outcome of the results is analyzed through measuring the prediction Accuracy, Sensitivity and Specificity to demonstrate the improvisation and effectiveness of the proposal in comparison to a few existing classifiers.

Software Defect Distribution Prediction for BOSS System

2014 ◽  
Vol 701-702 ◽  
pp. 67-70
Author(s):  
Wan Jiang Han ◽  
He Yang Jiang ◽  
Yi Sun ◽  
Tian Bo Lu
Keyword(s):  
Software Development ◽  
Model Experiment ◽  
Development Process ◽  
Distribution Model ◽  
Defect Prediction ◽  
Software Development Process ◽  
Software Defects ◽  
Defect Distribution ◽  
Software Defect ◽  
Important Activity

Effective detection of software defects is an important activity of software development process. In this paper, we propose an approach to predict residual defects for BOSS project, which applies defect distribution model. Experiment results show that this approach can effectively improve the accuracy of defect prediction.

EMPIRICAL EVALUATION OF CLASSIFIERS FOR SOFTWARE RISK MANAGEMENT

2009 ◽  
Vol 08 (04) ◽  
pp. 749-767 ◽  
Author(s):  
YI PENG ◽  
GANG KOU ◽  
GUOXUN WANG ◽  
HONGGANG WANG ◽  
FRANZ I. S. KO
Keyword(s):  
Risk Management ◽  
Software Development ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Performance Metric ◽  
Software Modules ◽  
Software Risk Management ◽  
Software Risk ◽  
Prediction Techniques

Software development involves plenty of risks, and errors exist in software modules represent a major kind of risk. Software defect prediction techniques and tools that identify software errors play a crucial role in software risk management. Among software defect prediction techniques, classification is a commonly used approach. Various types of classifiers have been applied to software defect prediction in recent years. How to select an adequate classifier (or set of classifiers) to identify error prone software modules is an important task for software development organizations. There are many different measures for classifiers and each measure is intended for assessing different aspect of a classifier. This paper developed a performance metric that combines various measures to evaluate the quality of classifiers for software defect prediction. The performance metric is analyzed experimentally using 13 classifiers on 11 public domain software defect datasets. The results of the experiment indicate that support vector machines (SVM), C4.5 algorithm, and K-nearest-neighbor algorithm ranked the top three classifiers.

scholarly journals Cost Effectiveness of Software Defect Prediction in an Industrial Project

2018 ◽  
Vol 43 (1) ◽  
pp. 7-35 ◽  
Author(s):  
Jaroslaw Hryszko ◽  
Lech Madeyski
Keyword(s):  
Quality Assurance ◽  
Cost Effectiveness ◽  
Software Development ◽  
Development Project ◽  
Defect Prediction ◽  
Cost Ratio ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Measurement Framework ◽  
The Cost

Abstract Software defect prediction is a promising approach aiming to increase software quality and, as a result, development pace. Unfortunately, the cost effectiveness of software defect prediction in industrial settings is not eagerly shared by the pioneering companies. In particular, this is the first attempt to investigate the cost effectiveness of using the DePress open source software measurement framework (jointly developed by Wroclaw University of Science and Technology, and Capgemini software development company) for defect prediction in commercial software projects. We explore whether defect prediction can positively impact an industrial software development project by generating profits. To meet this goal, we conducted a defect prediction and simulated potential quality assurance costs based on the best possible prediction results when using a default, non-tweaked DePress configuration, as well as the proposed Quality Assurance (QA) strategy. Results of our investigation are optimistic: we estimated that quality assurance costs can be reduced by almost 30% when the proposed approach will be used, while estimated DePress usage Return on Investment (ROI) is fully 73 (7300%), and Benefits Cost Ratio (BCR) is 74. Such promising results, being the outcome of the presented research, have caused the acceptance of continued usage of the DePress-based software defect prediction for actual industrial projects run by Volvo Group.

scholarly journals Bottlenecks in Software Defect Prediction Implementation in Industrial Projects

2015 ◽  
Vol 40 (1) ◽  
pp. 17-33 ◽  
Author(s):  
Jarosław Hryszko ◽  
Lech Madeyski
Keyword(s):  
Software Development ◽  
Defect Prediction ◽  
Software Systems ◽  
Development Projects ◽  
Software Defect Prediction ◽  
Software Defect ◽  
University Of Technology ◽  
Industrial Projects ◽  
Industrial Software ◽  
Extensible Software

Abstract Case studies focused on software defect prediction in real, industrial software development projects are extremely rare. We report on dedicated R&D project established in cooperation between Wroclaw University of Technology and one of the leading automotive software development companies to research possibilities of introduction of software defect prediction using an open source, extensible software measurement and defect prediction framework called DePress (Defect Prediction in Software Systems) the authors are involved in. In the first stage of the R&D project, we verified what kind of problems can be encountered. This work summarizes results of that phase.

An optimized Software defect prediction model based on PSO - ANFIS

2020 ◽  
Vol 13 ◽  
Author(s):  
Misha Kakkar ◽  
Sarika Jain ◽  
Abhay Bansal ◽  
P.S. Grover
Keyword(s):  
Software Development ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Anfis Model ◽  
Inference System ◽  
Model Based ◽  
Software Defect ◽  
Novel Approach ◽  
Development Teams ◽  
Software Development Teams

Introduction : The Software defect prediction (SDP) model plays a very important role in today’s software industry. SDP models can provide either only a list of defect-prone classes as output or the number of defects present in each class. This output can then be used by quality assurance teams to effectively allocate limited resources for validating software products by putting more effort into these defect-prone classes.The study proposes an OANFIS-SDP model that gives the number of defects as an output to software development teams. Development teams can then use this data for better allocation for their scares resources such as time and manpower. Method: OANFIS is a novel approach based on the Adaptive neuro-fuzzy inference system (ANFIS), which is optimized using Particle swarm optimization (PSO). OANFIS model combines the flexibility of ANFIS model with the optimization capabilities of PSO for better performance. Results: The proposed model is tested using the dataset from open source java projects of varied sizes (from 176 to 745 classes). Conclusion: The study proposes an SDP model based OANFIS that gives the number of defects as an output to software development teams. Development teams can then use this data for better allocation for their scares resources such as time and manpower. OANFIS is a novel approach that uses the flexibility provided by the ANFIS model and optimizes the same using PSO. The results given by OANFIS are very good and it can also be concluded that the performance of the SDP model based on OANFIS might be influenced by the size of projects. Discussion: The performance of the SDP model based on OANFIS is better than the ANFIS model. It can also be concluded that the performance of the SDP model might be influenced by the size of projects.

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