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Are 20% of files responsible for 80% of defects?

Neil Walkinshaw, Leandro Minku
2018 Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement - ESEM '18  
If we count a x that spans multiple les as multiple separate defects, then the principle holds; 20% of les are responsible for (almost exactly) 80% of defects.  ...  Previous empirical studies indicate that so ware defects obey the Pareto Principle that a minority of modules or les (the top 20%) are responsible for a majority of defects (around 80%) [3, 8, 11, 16,  ... 
doi:10.1145/3239235.3239244 dblp:conf/esem/WalkinshawM18 fatcat:hxubrqayabbafll7onr6ngw56a

Contemporary COBOL: Developers' Perspectives on Defects and Defect Location [article]

Agnieszka Ciborowska, Aleksandar Chakarov, Rahul Pandita
2021 arXiv   pre-print
Mainframe systems are facing a critical shortage of developer workforce as the current generation of COBOL developers retires.  ...  While we made substantial advances in the field of software maintenance for modern programming languages yearly, mainframe maintenance has received limited attention.  ...  ACKNOWLEDGMENT We thank our participants for their time and effort, Dan Acheff for his insights about COBOL ecosystem, and Brad Cleavenger and Greg Brueggeman for their help in designing the survey.  ... 
arXiv:2105.01830v2 fatcat:3g4d2tnfpbhwtmpjft3r4zbjoy

The Derivation of Defect Priorities and Core Defects through Impact Relationship Analysis between Embedded Software Defects

Sang Moo Huh, Woo-Je Kim
2020 Applied Sciences  
However, in the embedded software field, studies that have collected and categorized relevant defects into an integrated perspective are scarce, and none of them have identified core defects.  ...  In the pure software field, a method of deriving core defects already exists, enabling the collection and classification of all possible defects.  ...  Research procedure for classification of collected defects and derivation of core defects.Figure 1. Research procedure for classification of collected defects and derivation of core defects.  ... 
doi:10.3390/app10196946 fatcat:cnem7ys34jhhvm3zy67qdvos5y

Sample size vs. bias in defect prediction

Foyzur Rahman, Daryl Posnett, Israel Herraiz, Premkumar Devanbu
2013 Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering - ESEC/FSE 2013  
While this is increasingly the case in Empirical Software Engineering, some of the most popular bug-fix datasets are now known to be biased.  ...  Biased datasets are sampling only some of the data that could be sampled, and doing so in a biased fashion; but biased samples could be smaller, or larger.  ...  ., severe defects are linked at a median rate of about 80% and less severe defects at about 75%.  ... 
doi:10.1145/2491411.2491418 dblp:conf/sigsoft/RahmanPHD13 fatcat:24k6ctj4dbh4xcavkacks6pne4

Perceptions, Expectations, and Challenges in Defect Prediction

Zhiyuan Wan, Xin Xia, Ahmed E. Hassan, David Lo, Jianwei Yin, Xiaohu Yang
2018 IEEE Transactions on Software Engineering  
Through a qualitative analysis of free-form text responses, we identified reasons why practitioners are reluctant to adopt defect prediction tools.  ...  Defect prediction has been an active research area for over four decades. Despite numerous studies on defect prediction, the potential value of defect prediction in practice remains unclear.  ...  ACKNOWLEDGMENTS The authors would like to thank all survey participants for responding our survey.  ... 
doi:10.1109/tse.2018.2877678 fatcat:da5ehjqssjbjpn2jkktfi334za

Estimation of Defects Based on Defect Decay Model: ED^{3}M

S.W. Haider, J.W. Cangussu, K.M.L. Cooper, R. Dantu
2008 IEEE Transactions on Software Engineering  
Here, a new approach, called Estimation of Defects based on Defect Decay Model (ED 3 M) is presented that computes an estimate of the total number of defects in an ongoing testing process.  ...  An accurate prediction of the number of defects in a software product during system testing contributes not only to the management of the system testing process but also to the estimation of the product's  ...  Their research [5] has shown that faults are distributed in files according to the famous Pareto Principle, i.e., 80 percent of the faults are found in 20 percent of the files.  ... 
doi:10.1109/tse.2008.23 fatcat:ttlpi3ugg5cttpihhtegmzyesi

Defect Reduction Planning (using TimeLIME) [article]

Kewen Peng, Tim Menzies
2021 arXiv   pre-print
In this case study, it was not difficult to augment a standard AI algorithm with SE knowledge (that past releases are a good source of knowledge for planning defect reductions).  ...  Hence, we strongly recommend using past releases as a source of knowledge for computing fixes for new releases (using TimeLIME).  ...  ACKNOWLEDGEMENTS This work was partially funded by a research grant from the National Science Foundation (CCF #1703487) and the Laboratory for Analytical Sciences, North Carolina State University.  ... 
arXiv:2006.07416v2 fatcat:6kiijvxpzbfd7nlhgercrf7s5e

Recalling the "imprecision" of cross-project defect prediction

Foyzur Rahman, Daryl Posnett, Premkumar Devanbu
2012 Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering - FSE '12  
Thus, we argue that measures based on a variety of tradeoffs, viz., 5%, 10% or 20% of files tested/inspected would be more suitable. We study cross-project defect prediction from this perspective.  ...  We argue that these IR-based measures, while broadly applicable, are not as well suited for the quality-control settings in which defect prediction models are used.  ...  This means, more than 80% of the resolved defects got linked to the files where the defects were fixed.  ... 
doi:10.1145/2393596.2393669 dblp:conf/sigsoft/RahmanPD12 fatcat:l2ywfzdn4vdwrnejhanq7h3una

Predicting defects using change genealogies

Kim Herzig, Sascha Just, Andreas Rau, Andreas Zeller
2013 2013 IEEE 24th International Symposium on Software Reliability Engineering (ISSRE)  
In this paper, we show that change genealogies offer good classification models when identifying defective source files: With a median precision of 73% and a median recall of 76%, change genealogy defect  ...  Sometimes however, there are indirect effects that count: Changing a module may lead to plenty of follow-up modifications in other places, making the initial change having an impact on those later changes  ...  We thank the reviewers for their constructive comments.  ... 
doi:10.1109/issre.2013.6698911 dblp:conf/issre/HerzigJRZ13 fatcat:vyg7jjib6bdj7dvkweoayuwdci

Follow-up survey of parents of children with major birth defects in New York State

Monica Sharpe-Stimac, Ying Wang, Charlotte M. Druschel, Philip K. Cross
2004 Birth defects research. Clinical and molecular teratology  
INTRODUCTION Major birth defects are diagnosed in 3 to 4 percent of infants in their first year of life; about 120,000 U.S. babies are born each year with birth defects.  ...  For the other services listed in the information sheets, 60-80% of the respondents who had contacted them found them helpful.  ... 
doi:10.1002/bdra.20069 pmid:15368559 fatcat:icknbzfb2vhv7brkhjz5akfit4

Heterogeneous defect prediction

Jaechang Nam, Sunghun Kim
2015 Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering - ESEC/FSE 2015  
As a result, current techniques for CPDP are difficult to apply across projects with heterogeneous metric sets.  ...  Software defect prediction is one of the most active research areas in software engineering.  ...  Against CPDP-IFS, the Win results of HDP account for more than 80% after appying the metric selection approaches.  ... 
doi:10.1145/2786805.2786814 dblp:conf/sigsoft/NamK15 fatcat:cibql5v3ifeeblocrgufhrug4y

Better cross company defect prediction

Fayola Peters, Tim Menzies, Andrian Marcus
2013 2013 10th Working Conference on Mining Software Repositories (MSR)  
Accordingly, this filter selects training data via the structure of other projects. To assess the performance of the Peters filter, we compare it with two other approaches for quality prediction.  ...  This paper finds that: 1) within-company predictors are weak for small data-sets; 2) the Peters filter+cross-company builds better predictors than both within-company and the Burak filter+cross-company  ...  The metric is known also as Class Interface Size (CIS) response for a class rfc number of methods invoked in response to a message to the object weighted methods per class wmc the number of methods in  ... 
doi:10.1109/msr.2013.6624057 dblp:conf/msr/PetersMM13 fatcat:bfygn3jpavfernzbc3g5i4dok4

A systematic data collection procedure for software defect prediction

Goran Mausa, Tihana Galinac-Grbac, Bojana Dalbelo-Basic
2016 Computer Science and Information Systems  
To achieve greater generalization of the results, standardized protocols for data collection and validation are necessary.  ...  Software defect prediction research relies on data that must be collected from otherwise separate repositories.  ...  This limitation impacts 29 of the analyzed files and is therefore responsible for 58% of the files with different numbers of bugs (N b o ).  ... 
doi:10.2298/csis141228061m fatcat:wq3sx4fiy5ek7fkdr43zilsoju

Test coverage and post-verification defects: A multiple case study

Audris Mockus, Nachiappan Nagappan, Trung T. Dinh-Trong
2009 2009 3rd International Symposium on Empirical Software Engineering and Measurement  
This suggests that for most projects the optimal levels of coverage are likely to be well short of 100%. 3. Going above 70% coverage leads into exception handling code.  ...  Test coverage is a promising measure of test effectiveness and development organizations are interested in costeffective levels of coverage that provide sufficient fault removal with contained testing  ...  Logistic regression for Avaya project with response indicating post-SV MR. There are 2472 observations and the three predic- tors explain 20% of the deviance.  ... 
doi:10.1109/esem.2009.5315981 dblp:conf/esem/MockusND09 fatcat:77yihunzjbaklhedhi7r3whidy

Rotation Forest in Software Defect Prediction

Goran Mausa, Nikola Bogunovic, Tihana Galinac Grbac, Bojana Dalbelo Basic
2015 Software Quality Analysis, Monitoring, Improvement, and Applications  
Classification models are the main tool for performing the prediction and the search for a model of utmost performance is an ongoing activity.  ...  Software Defect Prediction (SDP) deals with localization of potentially faulty areas of the source code.  ...  It follows the Pareto principle, meaning that minority of source code (20%) is responsible for majority of defects (80%) [Galinac Grbac et al., 2013] .  ... 
dblp:conf/sqamia/MausaBGB15 fatcat:f7wqy5ylkbhurjhy4tuldviqsi
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