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Feature-Oriented Defect Prediction: Scenarios, Metrics, and Classifiers [article]

Mukelabai Mukelabai, Stefan Strüder, Daniel Strüber, Thorsten Berger
2021 arXiv   pre-print
We covered scenarios such as just-in-time (JIT) and cross-project defect prediction. Our results confirm the feasibility of feature-oriented defect prediction.  ...  We explore the feasibility and solution space for feature-oriented defect prediction.  ...  two new metric sets designed for feature-oriented defect prediction; (iii) selecting and training 7 classifiers we considered for our evaluation; and (iv) evaluating 5 different defect prediction scenarios  ... 
arXiv:2104.06161v1 fatcat:w6gsqtxqlnhg5asqbbanyebmqu

Towards predicting feature defects in software product lines

Rodrigo Queiroz, Thorsten Berger, Krzysztof Czarnecki
2016 Proceedings of the 7th International Workshop on Feature-Oriented Software Development - FOSD 2016  
Our best scenario achieves an accuracy of 73 % for accurately predicting features as defective or clean using a Naive Bayes classifier. Based on the results we discuss directions for future work.  ...  We adapt process metrics and evaluate and compare three classifiers using an open-source product line. Our results show that the technique can be effective.  ...  Study Design Our objective is to create a defect-prediction model for features and to evaluate three classifiers on a real system.  ... 
doi:10.1145/3001867.3001874 dblp:conf/oopsla/QueirozBC16 fatcat:gbx4axu7sze4xcaylruiq6i244

Examining the Predictive Capability of Advanced Software Fault Prediction Models – An Experimental Investigation Using Combination Metrics

Issam Al-Azzoni, Saqib Iqbal
2022 e-Informatica Software Engineering Journal  
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version  ...  This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article.  ...  Malhotra [15] Product metrics The study uses a logistic regression-based classifier on object-oriented metrics data set to predict the software fault proneness.  ... 
doi:10.37190/e-inf220104 fatcat:a56dnlth5jadnl6rqdixefwusy

An empirical study on software defect prediction with a simplified metric set

Peng He, Bing Li, Xiao Liu, Jun Chen, Yutao Ma
2015 Information and Software Technology  
and is very useful in case limited resources are supplied; (3) simple classifiers (e.g., Naive Bayes) also tend to perform well when using a simplified metric set for defect prediction; and (4) in several  ...  The objective of this work is to validate the feasibility of the predictor built with a simplified metric set for software defect prediction in different scenarios, and to investigate practical guidelines  ...  Acknowledgment We greatly appreciate the constructive comments and useful suggestions from the editor and anonymous reviewers, which help us improve the quality and readability of our paper.  ... 
doi:10.1016/j.infsof.2014.11.006 fatcat:wq6dgwcnjnantn5it62zvyltfa

Software Defect Prediction using Deep Learning

Meetesh Nevendra, Pradeep Singh
2021 Acta Polytechnica Hungarica  
The primary purpose of ML techniques in Software Defect Prediction (SDP) is to predict defects, according to historical data.  ...  The proposed approach has been evaluated on nineteen opensource software defect datasets, with respect to different evaluation metrics.  ...  Future research will focus on time reduction and accelerated network training. Also, exploration of other software metrics, aimed at the development of more efficient DL models, will be considered.  ... 
doi:10.12700/aph.18.10.2021.10.9 fatcat:v57mtvomhneinlct6vy2g7hsbu

An empirical study on predicting defect numbers

Mingming Chen, Yutao Ma
2015 Proceedings of the 27th International Conference on Software Engineering and Knowledge Engineering  
Many methods have been proposed to predict the defect-proneness of software components using supervised classification techniques in within-and cross-project scenarios.  ...  Defect prediction is an important activity to make software testing processes more targeted and efficient.  ...  With the rapid development of object-oriented programming and software process management techniques, some of new prediction models began to utilize more types of metrics to predict defect numbers by means  ... 
doi:10.18293/seke2015-132 dblp:conf/seke/ChenM15 fatcat:6mvnp3mt2zhjdfw3wmsimhvomi

Heterogeneous Defect Prediction via Exploiting Correlation Subspace

Ming Cheng, Guoqing Wu, Min Jiang, Hongyan Wan, Guoan You, Mengting Yuan
2016 Proceedings of the 28th International Conference on Software Engineering and Knowledge Engineering  
Most previous efforts assumed the cross-project defect data have the same metrics set which means the metrics used and size of metrics set are same in the data of projects.  ...  However, in real scenarios, this assumption may not hold. In addition, software defect datasets have the class imbalance problem increasing the difficulty for the learner to predict defects.  ...  We consider this scenarios as Heterogeneous Cross-Project Defect Prediction (HCPDP) [7] [8] .  ... 
doi:10.18293/seke2016-090 dblp:conf/seke/ChengWJWYY16 fatcat:i7ojyyyakjgprlnmryivlf2zze

Which is better? A Modularized Evaluation for Topic Popularity Prediction [article]

Yiming Zhang, Jiacheng Luo, Xiaofeng Gao, Guihai Chen
2017 arXiv   pre-print
Furthermore, we analyze the efficiency and contribution of features used in feature oriented methods.  ...  The results show that feature oriented methods are more suitable for scenarios requiring high accuracy, while relation based methods have better consistency.  ...  We classify all the methods into two categories: feature oriented methods and relation based methods in the first module.  ... 
arXiv:1710.05526v1 fatcat:krzpe2wxlbfffpybuom3thhfju

An Empirical Study for Enhanced Software Defect Prediction Using a Learning-Based Framework

Kamal Bashir, Tianrui Li, Chubato Wondaferaw Yohannese
2018 International Journal of Computational Intelligence Systems  
We apply the proposed framework on three software metrics, namely static code metric (SCM), object oriented metric (OOM), and combined metric (CombM) and build models based on four scenarios (S): (S1)  ...  A B S T R A C T ID:p0070 The object of software defect prediction (SDP) is to identify defect-prone modules.  ...  Comparing classification performance in all scenarios using static code metric (SCM), object-oriented metric (OOM), and combined metric (CombM). this end, multiple pairwise comparisons using the least  ... 
doi:10.2991/ijcis.2018.125905638 fatcat:l4hemkm3afgrvkbcugn36hvbdm

Object oriented quality prediction through artificial intelligence and machine learning: a survey

Jitendrea Kumar Saha, Kailash Patidar, Rishi Kushwah, Gaurav Saxena
2020 ACCENTS Transactions on Information Security  
Object- oriented quality metrics prediction can help in the estimation of software quality of any defects and the chances of errors.  ...  In this paper a survey and the case analytics have been presented for the object-oriented quality prediction. It shows the analytical and experimental aspects of previous methodologies.  ...  They have considered different object-oriented metrics. It has been considered based on feature amenability and changes.  ... 
doi:10.19101/tis.2020.517005 fatcat:vqsixyz3nbco7m4i2tpjoymvgu

An Empirical Study of Software Metrics Diversity for Cross-Project Defect Prediction

Yiwen Zhong, Kun Song, ShengKai Lv, Peng He, Chunlai Chai
2021 Mathematical Problems in Engineering  
Cross-project defect prediction (CPDP) is a mainstream method estimating the most defect-prone components of software with limited historical data.  ...  Finally, we further verify the CPDP-OSS feasibility built with three types of metrics (orient-object, semantic, and structural metrics) and challenge them against two current models.  ...  Jaechang Nam and Dr. Sinno Jialin Pan, the authors of reference [46] , for providing us with the TCA source program and friendly teaching us how to use it.  ... 
doi:10.1155/2021/3135702 fatcat:bgeywkri5va3nkfkkkjzcxxpzm

Conditional Domain Adversarial Adaptation for Heterogeneous Defect Prediction

Lina Gong, Shujuan Jiang, Li Jiang
2020 IEEE Access  
They generated a large number of classifiers to predict defective modules by genetic learning and integrated learning phases.  ...  Jureczko and Madeyski [23] collected MORPH repository from the online PROMISE data repository. Each project in MORPH has 20 metrics including McCabe's cyclomatic metrics, object-oriented metrics.  ... 
doi:10.1109/access.2020.3017101 fatcat:jtkwyozujzfg5intlo223gkkfm

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.  ...  It describes the key areas of software defect prediction practice, and highlights some key open issues for the future.  ...  -Use of software science metrics is ineffective for defect prediction and classification of defect prone modules in object oriented software.  ... 
doi:10.4236/jsea.2011.411075 fatcat:4myguuo6gngzfczxm6je4snxy4

Heterogeneous defect prediction

Jaechang Nam, Sunghun Kim
2015 Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering - ESEC/FSE 2015  
We can build a prediction model with defect data collected from a software project and predict defects in the same project, i.e. within-project defect prediction (WPDP).  ...  Our HDP approach conducts metric selection and metric matching to build a prediction model between projects with heterogeneous metric sets.  ...  Figure 1 : 1 Various Defect Prediction Scenarios PROMISE repository have 37 metrics but AEEEM datasets used by D'Ambroas et al. have 61 metrics Figure 2 : 2 Heterogeneous defect prediction rics are matched  ... 
doi:10.1145/2786805.2786814 dblp:conf/sigsoft/NamK15 fatcat:cibql5v3ifeeblocrgufhrug4y

Hybrid Ensemble Learning Technique for Software Defect Prediction

Mohammad Zubair Khan, Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, KSA
2020 International Journal of Modern Education and Computer Science  
During the past two decades, various approaches to software defect prediction that rely on software metrics have been proposed.  ...  Scientific data are used to predict the software's future release. Study shows that machine learning and hybrid algorithms are change benchmarks in the prediction of defects.  ...  Malhotra [4] used MLTs based on object-oriented software metrics (OOSM) for software defect prediction.  ... 
doi:10.5815/ijmecs.2020.01.01 fatcat:h3peins65fdxbeizmqo4rhbcly
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