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Evaluating predictive quality models derived from software measures: Lessons learned

Filippo Lanubile, Giuseppe Visaggio
1997 Journal of Systems and Software  
1 This paper describes an empirical comparison of several modeling techniques for predicting the quality of software components early in the software life cycle.  ...  Using software product measures, we built models that classify components as high-risk, i.e., likely to contain faults, or low-risk, i.e., likely to be free of faults.  ...  Acknowledgments We would like to thank the students from the University of Bari for providing the fault data used in this study, Aurora Lonigro and Giulia Festino for their support in processing and analyzing  ... 
doi:10.1016/s0164-1212(96)00153-7 fatcat:pzllwytbkvf4hipgn6hdvcyjua

An architectural model for software testing lesson learned systems

Javier Andrade, Juan Ares, María-Aurora Martínez, Juan Pazos, Santiago Rodríguez, Julio Romera, Sonia Suárez
2013 Information and Software Technology  
Objective: We defend the use of a lesson learned system for software testing.  ...  This model (i) defines the structure of the software testing lessons learned; (ii) sets up procedures for lesson learned management; and (iii) supports the design of software tools to manage the lessons  ...  The proposal manages a knowledge map, which is also used as a knowledge yellow pages, and knowledge classification trees to classify the documents considered important for solving software testing problems  ... 
doi:10.1016/j.infsof.2012.03.003 fatcat:p2lko7fl5ra7hc76jadef67itm

Lessons Learned for Effective FMEAs [chapter]

2012 Effective FMEAs  
He has 20 years experience in reliability engineering and management positions at General Motors, most recently Senior Manager for the Advanced Reliability Group.  ...  The four broad success factors (understanding the basics of FMEAs and Risk Assessment, applying key factors for effective FMEAs, providing excellent FMEA facilitation and implementing a "best practice"  ...  The mistake is: Disconnect between FMEA and field lessons learned Quality Objective # 5 The FMEA considers all major "lessons learned" (such as high warranty, campaigns, etc.) as input for failure mode  ... 
doi:10.1002/9781118312575.ch9 fatcat:otvbgyjoezfvfdwfkrmnbji4hm

Machine Learning and Radiogenomics: Lessons Learned and Future Directions

John Kang, Tiziana Rancati, Sangkyu Lee, Jung Hun Oh, Sarah L. Kerns, Jacob G. Scott, Russell Schwartz, Seyoung Kim, Barry S. Rosenstein
2018 Frontiers in Oncology  
Radiation oncology is particularly suited for predictive machine learning (ML) models due to the enormous amount of diagnostic data used as input and therapeutic data generated as output.  ...  We end with important lessons for the proper integration of ML into radiogenomics.  ...  Lessons From Statistics For ML models to focus on predictive performance alone while not taking lessons from statistical theory would be a mistake.  ... 
doi:10.3389/fonc.2018.00228 pmid:29977864 pmcid:PMC6021505 fatcat:j76dirpl6zbate3nqn6w6owvlm

Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP models [article]

Alena Fenogenova, Maria Tikhonova, Vladislav Mikhailov, Tatiana Shavrina, Anton Emelyanov, Denis Shevelev, Alexandr Kukushkin, Valentin Malykh, Ekaterina Artemova
2022 arXiv   pre-print
most recent models for Russian.  ...  This paper presents Russian SuperGLUE 1.1, an updated benchmark styled after GLUE for Russian NLP models.  ...  Introduction In the last years, new architectures and methods for model pre-training and transfer learning have driven striking performance improvements across a range of language understanding tasks.  ... 
arXiv:2202.07791v1 fatcat:3yzjribv7zgonly55na2orldku

Experimental evaluation of a tool for change impact prediction in requirements models: Design, results, and lessons learned

Arda Goknil, Roderick van Domburg, Ivan Kurtev, Klaas van den Berg, Fons Wijnhoven
2014 2014 IEEE 4th International Model-Driven Requirements Engineering Workshop (MoDRE)  
It is hypothesized that using TRIC would positively impact the quality of change impact predictions. Two formal hypotheses are developed.  ...  We developed a tool that uses formal semantics of requirements relations to support change impact analysis and prediction in requirements models.  ...  This research requires the creation of a classification scheme for levels of software tool intelligence. TABLE I .  ... 
doi:10.1109/modre.2014.6890826 dblp:conf/re/GoknilDKBW14 fatcat:x3smazqzbrbgpmoja63tertgeu

Learning from Corporate Memory and Best Practices [chapter]

Nada Matta, Oswaldo Castillo
2012 New Research on Knowledge Management Applications and Lesson Learned  
and the structure of the learning material,  Define infrastructures, resources and services necessary for distributing lessons and preserving their quality.  ...  Each team have to produce a report in which the related expertise is modelled using CommonKADS (for the first Project) and using MASK (for the second project). They have 2 months for each project.  ... 
doi:10.5772/32978 fatcat:sxa6xwylkbbnhebqrjg2nkxdgm

Lessons Learned Developing an Assembly System for WRS 2020 Assembly Challenge [article]

Aayush Naik, Priyam Parashar, Jiaming Hu, Henrik I. Christensen
2021 arXiv   pre-print
We present our approach to assembly based on integration of machine vision, robust planning and execution using behavior trees and a hierarchy of recovery strategies to ensure robust operation.  ...  Our system was selected for the WRS 2020 Assembly Challenge finals based on robust performance in the qualifying rounds. We present the systems approach adopted for the challenge.  ...  We would also like to thank Jack Griffin for his help with the finger designs.  ... 
arXiv:2103.15236v1 fatcat:rw6i3vl7wbgspmdgvgmymjefbe

Applying Model-Driven Engineering to High-Performance Computing: Experience Report, Lessons Learned, and Remaining Challenges

Benoît Lelandais, Marie-Pierre Oudot, Benoît Combemale
2019 Journal of Computer Languages  
From this experience, we discuss the main lessons learned to be considered for conducting future projects in the field of HPC, and the remaining challenges that are worth being included in the road-map  ...  In this paper, we report on our experience in the use of Model-Driven Engineering (MDE) and Domain-Specific Languages (DSLs) to face these challenges through two projects, namely Modane and NabLab.  ...  Lessons Learned In this section, we discuss the main lessons learned from the implementation of the two projects introduced in the previous section.  ... 
doi:10.1016/j.cola.2019.100919 fatcat:co6uq4omtnh25ld2zvkrnu3do4

Cognitive Load Monitoring with Wearables — Lessons Learned from a Machine Learning Challenge

Martin Gjoreski, Bhargavi Mahesh, Tine Kolenik, Jens Uwe-Garbas, Dominik Seuss, Hristijan Gjoreski, Mitja Lustrek, Matjaz Gams, Veljko Pejovic
2021 IEEE Access  
The participants developed machine learning methods for cognitive load classification using wrist-worn physiological sensors' data, namely heart rate, R-R intervals, skin conductance, and skin temperature  ...  The results indicate that the most robust methods used multimodal sensor data, classical classification approaches such as decision trees and support vector machines or their ensembles, and Bayesian hyperparameter  ...  DISCUSSION AND LESSONS LEARNED Our meta-analysis presented in the previous section reveals the superiority of a combination of data processing techniques for the wrist-worn device-originated physiological  ... 
doi:10.1109/access.2021.3093216 fatcat:wu3ssydwibforpeeuuasw4h3fa

Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applications [article]

Lixuan Yang, Alessandro Finamore, Feng Jun, Dario Rossi
2021 arXiv   pre-print
The increasing success of Machine Learning (ML) and Deep Learning (DL) has recently re-sparked interest towards traffic classification.  ...  Summarizing our main findings, we gather that (i) while ML and DL models are both equally able to provide satisfactory solution for classification of known traffic, however (ii) the non-linear feature  ...  ACKNOWLEDGEMENT We wish to thank the Editor and the anonymous Reviewers, whose feedback improved the quality of this paper.  ... 
arXiv:2104.03182v2 fatcat:pf7ict625vc53kqydgp2tz2eve

Analysis of Learning Patterns and Performance – a Case Study of 3-D Modeling Lessons in the K-12 Classrooms

Yi-Chieh Wu, Wen-Hung Liao
2020 IEEE Access  
Designing suitable 3D modeling software for children is a challenging mission. Evaluation with an integrated lesson plan is another difficult task.  ...  This research examines learning patterns when working with 3D modeling software.  ... 
doi:10.1109/access.2020.3029947 fatcat:gcdhnfooyjdtbilpgisumtvfey

Considerations for generating meaningful HRA data: Lessons learned from HuREX data collection

Yochan Kim
2020 Nuclear Engineering and Technology  
Highlights The quality of the data collected for HRA is essential to the quality of HRA results. This paper presents important considerations for the collection and analysis of empirical data.  ...  Although it is obvious that the quality of data is critical, the practices or considerations for securing data quality have not been sufficiently discussed.  ...  In this paper, we presented a number of considerations for ensuring high-quality HRA data following the lessons learned from the HuREX data extraction experience.  ... 
doi:10.1016/j.net.2020.01.034 fatcat:occ4ml5kbjadtet3b7nrgofpfy

Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving

Nada Lavrač, Hiroshi Motoda, Tom Fawcett, Robert Holte, Pat Langley, Pieter Adriaans
2004 Machine Learning  
This introductory paper to the special issue on Data Mining Lessons Learned presents lessons from data mining applications, including experience from science, business, and knowledge management in a collaborative  ...  Robert Holte would like to acknowledge the support for this research by the Natural Sciences and Engineering Research Council of Canada and by the Alberta Ingenuity Centre for Machine Learning.  ...  Acknowledgments Nada Lavrač would like to acknowledge the support for this research by the Slovenian Ministry of Education, Science and Sport and the European project Data Mining and Decision Support for  ... 
doi:10.1023/b:mach.0000035516.74817.51 fatcat:qfrnehdv3zfnjnwi6t55chrch4

Developing scientific confidence in HTS-derived prediction models: Lessons learned from an endocrine case study

Louis Anthony (Tony) Cox, Douglas Popken, M. Sue Marty, J. Craig Rowlands, Grace Patlewicz, Katy O. Goyak, Richard A. Becker
2014 Regulatory toxicology and pharmacology  
Based on the lessons learned, we propose a framework for documenting scientific confidence in HTS assays and the prediction models derived therefrom.  ...  Using a case study of HTS-derived models for predicting in vivo androgen (A), estrogen (E), thyroid (T) and steroidogenesis (S) endpoints in endocrine screening assays, we compare classification (fitting  ...  A focus on quality and reliability of the input data is warranted when constructing datasets for use in building prediction models.  ... 
doi:10.1016/j.yrtph.2014.05.010 pmid:24845243 fatcat:adptlly6vfclfl6gggugrwiid4
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