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Network Versus Code Metrics to Predict Defects: A Replication Study

Rahul Premraj, Kim Herzig
2011 2011 International Symposium on Empirical Software Engineering and Measurement  
This paper presents a replication of one such study conducted by Zimmermann and Nagappan [1] on Windows Server 2003 where the authors leveraged dependency relationships between software entities captured  ...  In order to corroborate the generality of their findings, we replicate their study on three open source Java projects, viz., JRuby, ArgoUML, and Eclipse.  ...  Network metrics are however new to the field of predicting defect-prone entities and remain to be evaluated across different types of software systems.  ... 
doi:10.1109/esem.2011.30 dblp:conf/esem/PremrajH11 fatcat:bftx24i5ivdeldq5j7eo5l2dbi

Fault Prediction with Static Software Metrics in Evolving Software: A Case Study in Apache Ant

Xue Han, Gongjun Yan
2022 Journal of Computer and Communications  
We apply four machine learning techniques to construct fault prediction models from the PROMISE data set and evaluate the effectiveness of using static software metrics to build fault prediction models  ...  One way to address this problem is to identify which components of the subject under the test are more error-prone and thus demand more testing efforts.  ...  Conflicts of Interest The authors declare no conflicts of interest regarding the publication of this paper.  ... 
doi:10.4236/jcc.2022.102003 fatcat:bwj5xvl2xjc35j4lgpbtyfrhj4

Towards improving statistical modeling of software engineering data: think locally, act globally!

Nicolas Bettenburg, Meiyappan Nagappan, Ahmed E. Hassan
2014 Empirical Software Engineering  
However, we find that analysts need to be aware of potential pitfalls when building local models: firstly, the choice of clustering algorithm and its parameters can have a substantial impact on model quality  ...  Much research in software engineering (SE) is focused on modeling data collected from software repositories.  ...  Fig. 1 : 1 Example of Statistical modeling of Software Engineering Data. Fig. 2 : 2 Overview of our approach for building global and local regression models. This process is repeated 10 times.  ... 
doi:10.1007/s10664-013-9292-6 fatcat:b7qsfrf27vdu3e27akcgawaeqq

Revisiting Process versus Product Metrics: a Large Scale Analysis [article]

Suvodeep Majumder, Pranav Mody, Tim Menzies
2021 arXiv   pre-print
Numerous methods can build predictive models from software data.  ...  Also, when reasoning in-the-large about hundreds of projects, it is better to use predictions from multiple models (since single model predictions can become confused and exhibit a high variance).  ...  models build using process and product metrics on all 3 types of test sets.  ... 
arXiv:2008.09569v3 fatcat:6yewqj7bbveipoiywhly5zltby

Investigating the Impact of Metric Aggregation Techniques on Defect Prediction [article]

Rawad Abou Assi
2015 arXiv   pre-print
In this paper, we explore the effect of nine aggregation techniques on the correlation between three types of code metrics, namely Lines of Code, McCabe, and Halstead metrics.  ...  We also find that more complex aggregations are no different than much simpler ones and that incorporating all aggregation types in the same model does not provide a significant improvement over using  ...  Hassan and Mr. Shane McIntosh for helping make this work possible.  ... 
arXiv:1503.08504v1 fatcat:6qqewlv3bve4pd5cxjohipx3xe

New Generation of Software Metrics

Giulio Concas, Giovanni Cantone, Ewan Tempero, Hongyu Zhang
2010 Advances in Software Engineering  
We hope you will find this issue stimulating and useful and look forward for your evaluable feedback. Giulio Concas Giovanni Cantone Ewan Tempero Hongyu Zhang  ...  such a measure that is repeatable across different systems.  ...  On one hand, engineering is about designing and building things and modeling, characterizing, monitoring, evaluating, defining, predicting, "prescripting", controlling, and changing processes and their  ... 
doi:10.1155/2010/913892 fatcat:4texzf7hfnhyrdkvjibkdgmsvm

Heterogeneous defect prediction

Jaechang Nam, Sunghun Kim
2015 Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering - ESEC/FSE 2015  
Software defect prediction is one of the most active research areas in software engineering.  ...  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).  ...  For example, we build a prediction model by Apache in ReLink and tested the model on velocity-1.4 in MORPH (Apache⇒velocity-1.4). 1 We did not conduct defect prediction across projects in the same group  ... 
doi:10.1145/2786805.2786814 dblp:conf/sigsoft/NamK15 fatcat:cibql5v3ifeeblocrgufhrug4y

Mining metrics to predict component failures

Nachiappan Nagappan, Thomas Ball, Andreas Zeller
2006 Proceeding of the 28th international conference on Software engineering - ICSE '06  
Using principal component analysis on the code metrics, we built regression models that accurately predict the likelihood of post-release defects for new entities.  ...  In an empirical study of the post-release defect history of five Microsoft software systems, we found that failure-prone software entities are statistically correlated with code complexity measures.  ...  We thank Melih Demir, Tom Zimmermann and many others for their helpful comments on earlier revisions of this paper.  ... 
doi:10.1145/1134285.1134349 dblp:conf/icse/NagappanBZ06 fatcat:5z7xsgy2tjhrtpdly6mpohbdwm

Applying Neuro-fuzzy Approach to build the Reusability Assessment Framework across Software Component Releases - An Empirical Evaluation

Vijai Kumar, Rajesh Kumar, Arun Sharma
2013 International Journal of Computer Applications  
The aim of this paper is to formulate, build, evaluate, validate and compare neuro-fuzzy approach in prediction of software reusability of software components during the subsequent releases of a software  ...  The analysis and results of the study shows that neuro-fuzzy provides better results as compare to Fuzzy Inference System and neural network but applicability of best approach depends on the data availability  ...  management of release R2, Apply the resource distribution and focus on the area, which is responsible for less reusable component software.  Step-4: repeat from step 1 for release R2.  ... 
doi:10.5120/12041-8047 fatcat:m6m6jokx45daxhcy5ywdhvdshe

sj-pdf-1-epb-10.1177_2399808320921208 - Supplemental material for Classifying settlement types from multi-scale spatial patterns of building footprints

Warren C Jochem, Douglas R Leasure, Oliver Pannell, Heather R Chamberlain, Patricia Jones, Andrew J Tatem
2020 Figshare  
Supplemental material, sj-pdf-1-epb-10.1177_2399808320921208 for Classifying settlement types from multi-scale spatial patterns of building footprints by Warren C Jochem, Douglas R Leasure, Oliver Pannell  ...  , Heather R Chamberlain, Patricia Jones and Andrew J Tatem in Environment and Planning B: Urban Analytics and City Science  ...  The authors acknowledge the use of the IRIDIS Higher Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work.  ... 
doi:10.25384/sage.12294467 fatcat:5ch7pdoatnfntboy266bkxyiq4

Open Source Community Health: Analytical Metrics and Their Corresponding Narratives

Sean P Goggins, Kevin Lumbard, Matt Germonprez
2021 Zenodo  
Limitations of current analysis methods focused on trace data alone are discussed, and reviewed in depth.  ...  using standard metrics is framed as an approach to consider for examining open source software health and sustainability.  ...  The aims of open source project health and sustainability metrics (metrics) research differ across disciplines.  ... 
doi:10.5281/zenodo.4627236 fatcat:nhj6rogurbbwvj7v6upf3nxg3a

Cross Project Software Fault Prediction At Design Phase

Pradeep Singh, Shrish Verma
2015 Zenodo  
Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data.  ...  The training data consists of metrics data and related fault data at function/module level.  ...  The findings and opinions in this study belong solely to the authors, and are not necessarily those of the sponsor.  ... 
doi:10.5281/zenodo.1106977 fatcat:6acok5loifgzher36fk2ieeu3y

A classification procedure for the effective management of changes during the maintenance process

L.C. Briand, V.R. Basili
1992 Proceedings Conference on Software Maintenance 1992  
During software operation, maintainers are often faced with numerous change requests.  ...  Selby and T. Phillips. "Metric Analysis and Data Validation across FORTRAN Projectg". IEEE Transactions on Software Engineering, SE-9(6):652-663, November 1983 [BW84]V. Basili and D.  ...  bc repeated and refined, allowing the model to evolve consistently as new data are collected.  ... 
doi:10.1109/icsm.1992.242526 dblp:conf/icsm/BriandB92 fatcat:xu4f3kruxja3teurqgnp7iopu4

Using Software Dependencies and Churn Metrics to Predict Field Failures: An Empirical Case Study

Nachiappan Nagappan, Thomas Ball
2007 First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007)  
Our analysis indicates the ability of software dependencies and churn measures to be efficient predictors of post-release failures.  ...  Commercial software development is a complex task that requires a thorough understanding of the architecture of the software system.  ...  That is, we randomly pick two-thirds (1384) of the binaries to build our prediction model and the remaining one-third (691) to verify the efficacy of the built model.  ... 
doi:10.1109/esem.2007.13 dblp:conf/esem/NagappanB07 fatcat:ha6x3mdsmvfgjjdwhbed4ynh7q

Searching for a Needle in a Haystack: Predicting Security Vulnerabilities for Windows Vista

Thomas Zimmermann, Nachiappan Nagappan, Laurie Williams
2010 2010 Third International Conference on Software Testing, Verification and Validation  
In this paper, we present a large-scale empirical study on Windows Vista, where we empirically evaluate the efficacy of classical metrics like complexity, churn, coverage, dependency measures, and organizational  ...  structure of the company to predict vulnerabilities and assess how well these software measures correlate with vulnerabilities.  ...  We would like to thank the Windows team at Microsoft and Brendan Murphy of MSR Cambridge for his help in understanding the data sources.  ... 
doi:10.1109/icst.2010.32 dblp:conf/icst/ZimmermannNW10 fatcat:5pklhczkn5ev7hwamirjbs2auy
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