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Defect Prediction using Combined Product and Project Metrics - A Case Study from the Open Source "Apache" MyFaces Project Family

Dindin Wahyudin, Alexander Schatten, Dietmar Winkler, A. Min Tjoa, Stefan Biffl
2008 Proceedings of the EUROMICRO Conference  
Only few prediction models consider information on the development process (project metrics) that seems relevant to quality improvement of the software product.  ...  In this paper, we investigate defect prediction with data from a family of widely used OSS projects based both on product and project metrics as well as on combinations of these metrics.  ...  To validate our prediction models we fit the model to historical data of releases, we use the average relative error (ARE) to evaluate forecast accuracy.  ... 
doi:10.1109/seaa.2008.36 dblp:conf/seaa/WahyudinSWTB08 fatcat:lbuhlkwhpvdzrbkviejjwr2j5y

Software Project Management Using Machine Learning Technique—A Review

Mohammed Najah Mahdi, Mohd Hazli Mohamed Zabil, Abdul Rahim Ahmad, Roslan Ismail, Yunus Yusoff, Lim Kok Cheng, Muhammad Sufyian Bin Mohd Azmi, Hayder Natiq, Hushalini Happala Naidu
2021 Applied Sciences  
an alternative way to efficiently reduce the project failure probabilities, and increasing the output ratio for growth, and it also facilitates analysis on software fault prediction based on accuracy.  ...  used in machine-learning management and the final classes of the results from the study, contribution of studies in the production, and the promotion of machine-learning project prediction.  ...  Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app11115183 fatcat:e7cfhrwyunfeld7cpngu4uifl4

Application of Artificial Neural Network for Software Reliability Growth Modeling with Testing Effort

Subburaj Ramasamy, Indhurani Lakshmanan
2016 Indian Journal of Science and Technology  
The proposed ANN based model provides consistent performance for both exponential and S-shaped growth of mean value functions witnessed in software projects.  ...  Background/Objectives: To design a relatively simple Software Reliability Growth Model (SRGM) with testing effort function using Artificial Neural Network approach.  ...  In 12 evaluated SRGMs by improving their predictive accuracy using historical projects failure data. Traditional parametric SRGMs have two characteristics in common.  ... 
doi:10.17485/ijst/2016/v9i29/90093 fatcat:irf34rrhlfb2jfyhame2tctw64

Evaluating long-term predictive power of standard reliability growth models on automotive systems

Rakesh Rana, Miroslaw Staron, Christian Berger, Jorgen Hansson, Martin Nilsson, Fredrik Torner
2013 2013 IEEE 24th International Symposium on Software Reliability Engineering (ISSRE)  
Software Reliability Growth Models (SRGMs) have been long used to assess the reliability of software systems; they are also used for predicting the defect inflow in order to allocate maintenance resources  ...  Although a number of models have been proposed and evaluated, much of the assessment of their predictive ability is studied for short term (e.g. last 10% of data).  ...  ACKNOWLEDGMENT The research presented here is done under the VISEE project which is funded by Vinnova and Volvo Cars jointly under the FFI programme (VISEE, Project No: DIARIENR: 2011-04438).  ... 
doi:10.1109/issre.2013.6698922 dblp:conf/issre/RanaSBHNT13 fatcat:2a4cm2jwvndg3iazkqzvp74luq

Software Reliability Modeling using Soft Computing Techniques: Critical Review

Kuldeep Singh Kaswan, Sunita Choudhary, Kapil Sharma
2015 International Journal of Information Technology and Computer Science  
Software reliability models assess the reliability by predicting faults for the software. Reliability is a real world phenomenon with many associated real-time problems.  ...  and can be used globally.  ...  They found that their models are better prediction models than some other statistical models [16] . Used connectionist models for software reliability prediction.  ... 
doi:10.5815/ijitcs.2015.07.10 fatcat:ab7t7unlcnhmtm6vad3ymvtlvy

A Study On Software Engineering Defect Prediction

2022 Data Analytics and Artificial Intelligence  
Dependent and independent variable are considered in Software bug prediction. To prevent defect based on software metrics software prediction model are used.  ...  M any data mining techniques and dataset repository are available to predict the software defects. Bug prediction technique is an important part in software engineering area for last one decade.  ...  Using firm and well known data mining techniques, researchers and specialists have started exploring their precious data in order to manage their projects in an improved and professional manner and produce  ... 
doi:10.46632/daai/2/1/1 fatcat:25anbqvp3vg4fjeutyfccly5ya

Defect Prediction Leads to High Quality Product

Naheed Azeem, Shazia Usmani
2011 Journal of Software Engineering and Applications  
So, defect prediction is very important in the field of software quality and software reliability. This paper gives you a vivid description about software defect prediction.  ...  Defect prediction is relatively a new research area of software quality assurance. A project team always aims to produce a quality product with zero or few defects.  ...  the insignificant predictors to form a reliability growth model.  ... 
doi:10.4236/jsea.2011.411075 fatcat:4myguuo6gngzfczxm6je4snxy4

Software Reliability Modeling using Soft Computing Techniques: Critical Review

Kaswan KS Choudhary S
2015 Journal of Information Technology & Software Engineering  
Further to this, we have also compared soft computing techniques in terms of software reliability modeling capabilities.  ...  In this paper, we have provided an overview of existing soft computing techniques, and then critically analyzed the work done by the various researchers in the field of software reliability.  ...  [25] proposed an artificial neural network model to improve the early reliability prediction for current projects/releases by reusing the failure data from past projects/releases.  ... 
doi:10.4172/2165-7866.1000144 fatcat:mar6yvi7ejenzhwa3bgmwkxheq

Using hybrid algorithm to estimate and predicate based on software reliability model

Zhen Li, MiaoMiao Yu, DongSheng Wang, HaiFeng Wei
2019 IEEE Access  
This paper uses five classic sets of software failure data to estimate the GO model parameters and make predictions and performs a variety of comparisons of the algorithm results.  ...  The software reliability is mainly obtained through modeling and estimating. The existing software reliability models are nonlinear, and the parameter estimation of these models is difficult.  ...  The parameters a and b of the G-O model, their selection will affect the accuracy of the model prediction. B.  ... 
doi:10.1109/access.2019.2917828 fatcat:j7cduzjhdngidjx52o472fra2q

Software Reliability Modeling with Test Coverage: Experimentation and Measurement with A Fault-Tolerant Software Project

Xia Cai, Michael R. Lyu
2007 The 18th IEEE International Symposium on Software Reliability (ISSRE '07)  
Traditional software reliability growth models use the execution time during testing for reliability estimation.  ...  Although testing time is an important factor in reliability, it is likely that the prediction accuracy of such models can be further improved by adding other parameters which affect the final software  ...  Acknowledgement The work described in this paper was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CUHK4150/07E).  ... 
doi:10.1109/issre.2007.17 dblp:conf/issre/CaiL07 fatcat:dvf33ji54bc4rcjb7snrqvm6di

A Distribution-Level Combinational Model to Improve Reliability Prediction Accuracy

Wenjun Xie
2017 International Journal of Performability Engineering  
We use mean squared error (MSE) to evaluate the historical predictive validity. The results show that our model is consistently stable and has lower MSE.  ...  To evaluate the effectiveness of the proposed model, we use the failure data sets (21 projects) available in public sources.  ...  Jinhee uses decision tree to select base models and to decide the weights according to the prediction accuracies of the selected base models [21] .  ... 
doi:10.23940/ijpe.17.06.p5.832843 fatcat:52dlphtij5clfljta4ydvx4fxe

Predict the number of remaining faults during inspection using actual inspection data, where as Stranded predict which files will contain the most faults in the next release
IJARCCE - Computer and Communication Engineering

K.RAMA KRISHNA, B.KAVITHA LAXMI, S.PRATHIBHA BHARATHI, A. RADHIKA
2014 IJARCCE  
Software reliability growth models (SRGMs) are also used to estimate the total number of faults to measure software reliability.  ...  Zhang and Mockus use data collected from previous projects to estimate the number of faults in a new project.  ...  Software reliability growth models (SRGMs) are also used to estimate the total number of faults to measure software reliability.  ... 
doi:10.17148/ijarcce.2014.31124 fatcat:ev4zoon5trc55erjjtrken27my

Predicting the Popularity of GitHub Repositories

Hudson Borges, Andre Hora, Marco Tulio Valente
2016 Proceedings of the The 12th International Conference on Predictive Models and Data Analytics in Software Engineering - PROMISE 2016  
Furthermore, specific models---generated using data from repositories that share the same growth trends---are recommended for repositories with slow growth and/or for repositories with less stars.  ...  These predictions are useful both to repository owners and clients, who usually want to know how their projects are performing in a competitive open source development market.  ...  We also found that specific models, i.e., models produced using repositories that share the same growth trend, can reduce the average prediction error and produce reliable predictions using less data.  ... 
doi:10.1145/2972958.2972966 dblp:conf/promise/BorgesHV16 fatcat:jw4s44bp65dqtgme2xlyj3lani

Real-Time High-Load Infrastructure Transaction Status Output Prediction Using Operational Intelligence and Big Data Technologies

Solomia Fedushko, Taras Ustyianovych, Michal Gregus
2020 Electronics  
and operations, taking into account the exponential growth of information and the growing trend of Big Data-based projects.  ...  The methods used in the study include machine learning models, data preprocessing, missing data imputation, SRE (site reliability engineering) indicators computation, quantitative research, and a qualitative  ...  Particular methods include traditional data preprocessing: data ranges of certain features; normalization as a way to improve model accuracy, PCA (principal component analysis) used to select the best  ... 
doi:10.3390/electronics9040668 fatcat:vwmlmlqkhvejpbdxmdtwqw4rqq

Defect Prediction over Software Life Cycle in Automotive Domain - State of the Art and Road Map for Future

Rakesh Rana, Miroslaw Staron, Jörgen Hansson, Martin Nilsson
2014 Proceedings of the 9th International Conference on Software Engineering and Applications  
Methods of software defect predictions provide useful information for optimal resource allocation and release planning; they also help track and model software and system reliability.  ...  In this paper we present an overview of defect prediction methods and their applicability in different software lifecycle phases in the automotive domain.  ...  ACKNOWLEDGEMENTS The research presented here is done under the VISEE project which is funded by Vinnova and Volvo Cars jointly under the FFI programme (VISEE, Project No: DIARIENR: 2011-04438).  ... 
doi:10.5220/0005099203770382 dblp:conf/icsoft/RanaSHN14 fatcat:axvjeqcdeng5xpzijaphzrib2y
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