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检索条件"任意字段=10th International Conference on Predictive Models in Software Engineering, PROMISE 2014"

共 161 条记录，以下是1-10 订阅

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10th international conference on predictive models in software engineering, promise 2014

10th International Conference on Predictive Models in Softwa...

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10th international conference on predictive models in software engineering, promise 2014

ISBN: (纸本)9781450328982

the proceedings contain 9 papers. the topics discussed include: a proposed method to evaluate and compare fault predictions across studies;on the influence of maintenance activity types on the issue resolution time;analyzing defect inflow distribution of automotive software projects;feature weighting techniques for CBR in software effort estimation studies: a review and empirical evaluation;exploiting prior-phase effort data to estimate the effort for the subsequent phases: a further assessment;the potential benefit of relevance vector machine to software effort estimation;benchmarking effort estimation models using archetypal analysis;social metrics included in prediction models on software engineering: a mapping study;and agile software development: a systematic literature review.

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Welcome to the 10th international conference on predictive models in software engineering (promise 2014)

ACM International Conference Proceeding Series

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ACM international conference Proceeding Series 2014年

作者： Wagner, Stefan Di Penta, Massimiliano Bener, Ayse Minku, Leandro Turhan, Burak Zhang, Hungyu

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promise 2022 - Proceedings of the 18th international conference on predictive models and Data Analytics in software engineering, co-located with ESEC/FSE 2022

PROMISE 2022 - Proceedings of the 18th International Confere...

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18th ACM international conference on predictive models and Data Analytics in software engineering, promise 2022, co-located with the 30th ACM Joint European software engineering conference and Symposium on the Foundations of software engineering, ESEC/FSE 2022

ISBN: (纸本)9781450398602

the proceedings contain 10 papers. the topics discussed include: improving the performance of code vulnerability prediction using abstract syntax tree information;measuring design compliance using neural language models: an automotive case study;feature sets in just-in-time defect prediction: an empirical evaluation;profiling developers to predict vulnerable code changes;predicting build outcomes in continuous integration using textual analysis of source code commits;LOGI: an empirical model of heat-induced disk drive data loss and its implications for data recovery;assessing the quality of Github copilot’s code generation;on the effectiveness of data balancing techniques in the context of ml-based test case prioritization;identifying security-related requirements in regulatory documents based on cross-project classification;and API + code = better code summary? insights from an exploratory study.

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Predicting Fairness of ML software Configurations 20

Predicting Fairness of ML Software Configurations

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20th international conference on predictive models and Data Analytics in software engineering (promise)

作者： Robles Herrera, Salvador Monjezi, Verya Kreinovich, Vladik Trivedi, Ashutosh Tizpaz-Niari, Saeid Univ Texas El Paso El Paso TX 79968 USA Univ Colorado Boulder CO 80309 USA

ISBN: (纸本)9798400706752

this paper investigates the relationships between hyperparameters of machine learning and fairness. Data-driven solutions are increasingly used in critical socio-technical applications where ensuring fairness is important. Rather than explicitly encoding decision logic via control and data structures, the ML developers provide input data, perform some pre-processing, choose ML algorithms, and tune hyperparameters (HPs) to infer a program that encodes the decision logic. Prior works report that the selection of HPs can significantly influence fairness. However, tuning HPs to find an ideal trade-off between accuracy, precision, and fairness has remained an expensive and tedious task. Can we predict the fairness of HP configuration for a given dataset? Are the predictions robust to distribution shifts? We focus on group fairness notions and investigate the HP space of 5 training algorithms. We first find that tree regressors and XG-Boots significantly outperformed deep neural networks and support vector machines in accurately predicting the fairness of HPs. When predicting the fairness of ML hyperparameters under temporal distribution shift, the tree regressors outperform the other algorithms with reasonable accuracy. However, the precision depends on the ML training algorithm, dataset, and protected attributes. For example, the tree regressor model was robust for training data shift from 2014 to 2018 on logistic regression and discriminant analysis HPs with sex as the protected attribute;but not for race and other training algorithms. Our method provides a sound framework to efficiently perform fine-tuning of ML training algorithms and understand the relationships between HPs and fairness.

关键词： ML Hyperparameters Fairness distribution shifts

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Another Look at Non-homogeneous Markovian software Reliability Modeling 10

Another Look at Non-homogeneous Markovian Software Reliabili...

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10th international conference on Dependable Systems and their Applications, DSA 2023

作者： Qiu, Nanxiang Li, Siqiao Dohi, Tadashi Okamura, Hiroyuki Hiroshima University Graduate School of Advanced Sciences and Engineering Higashi-Hiroshima739-8527 Japan

ISBN: (纸本)9798350304770

this paper explores yet another software reliability modeling framework based on non-homogeneous Markov processes (NHMPs). For two subclasses of NHMPs;generalized binomial processes (GBPs) and generalized Polya processes (GPPs), we formulate 22 novel NHMP-based software reliability models (SRMs) with 11 kinds of baseline intensity functions, which are different from the existing NHMP-based SRMs by Li et al. (2023). Our evaluation of NHMP-based SRMs focuses on assessing their goodness-of-fit and predictive performances using 8 sets of software fault detection time-interval data (group data). the results are compared with the infinite-failure NHPP-based SRMs. through comprehensive numerical experiments, we show that our new modeling framework could provide the strengths in the goodness-of-fit performance but have some limitations on the predictive performance in the early testing phase. © 2023 IEEE.

关键词： Markov processes

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promise 2019 - 15th international conference on predictive models and Data Analytics in software engineering

PROMISE 2019 - 15th International Conference on Predictive M...

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15th international conference on predictive models and Data Analytics in software engineering, promise 2019, co-located with the 13th international Symposium on Empirical software engineering and Measurement, ESEM 2019

ISBN: (纸本)9781450372336

the proceedings contain 11 papers. the topics discussed include: the technical debt dataset;which refactoring reduces bug rate?;on usefulness of the deep-learning-based bug localization models to practitioners;an evaluation of parameter pruning approaches for software estimation;patterns of effort contribution and demand and user classiï¬ücation based on participation patterns in NPM ecosystem;leveraging change intents for characterizing and identifying large-review-effort changes;prioritizing automated user interface tests using reinforcement learning;reviewer recommendation using software artifact traceability graphs;applying cross project defect prediction approaches to cross-company effort estimation;and from reports to bug-fix commits: a 10 years dataset of bug-fixing activity from 55 apacheâ€™s open source projects.

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Bug predictive models based on Data Analytics and Soft Computing Techniques: A Survey 10

Bug Predictive Models based on Data Analytics and Soft Compu...

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17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Jadhav, Veena Devale, Prakash Jadhav, Rohini Molawade, Mayuri Mohite, Sagar Bidwe, Ranjeet Vasant College of Engineering Department of Computer Engineering Pune India Lavale Pune India

ISBN: (纸本)9789380544472

As software projects become larger and more complex, bugs pose a significant threat to their consistency and efficiency. During early phases of software development, bug prediction essentially enhances vital aspects such as software efficiency reliability quality, cost of development. Hence, various software bug prediction techniques have been presented previously but there is no research that focuses on surveying logical analysis and Just-In-Time (JIT) bug predictions with the consideration of data analytics and soft computing techniques. Hence this research investigates Soft computing and hybrid logistic model by determining imperfect range of data, class labels with heuristics prediction of bugs. Also, survey data analytics techniques such as clustering and oversampling techniques with JIT prediction for forecasting software bug in future. the study also compares various software bug prediction techniques to determine their effectiveness. then, mentioned the basic benefits and drawbacks of the various bug prediction techniques. Moreover, future directions are provided to overcome the existing impact of various techniques in software bug prediction. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Data Analytics

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Regression-Based Approach for Proactive predictive Modeling of Efficient Cloud Cost Estimation 10

Regression-Based Approach for Proactive Predictive Modeling ...

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10th international conference on software Defined Systems, SDS 2023

作者： Daraghmeh, Mustafa Agarwal, Anjali Jararweh, Yaser Concordia University Electrical and Computer Engineering Montreal Canada Jordan University of Science and Technology Computer Science Irbid Jordan

ISBN: (纸本)9798350382235

Businesses increasingly lean on cloud-based solutions in the contemporary digital landscape, drawn by their scalability and adaptability. Navigating the financial intricacies of cloud subscription models, particularly when intertwined with software-defined systems, remains a formidable challenge. this challenge is accentuated by dynamic pricing structures, making accurate cost forecasting a critical but complex endeavor. this research unveils an innovative methodology designed to meticulously forecast the financial costs of cloud subscription tasks based on resource allocation characteristics. Our approach centers around a robust model carefully created through particular preparation and modeling stages that incorporate a pricing model for various virtual machines and utilize different advanced regression algorithms to navigate the complex world of cloud subscription services. the results of our study, which analyzed real cloud workloads, indicate that equipping businesses with a powerful predictive tool can lead to improved financial planning and strategic decision-making in their cloud operations. this study aims to guide businesses in the ever-changing field of cloud technology, focusing on promoting financial efficiency and improving decision-making strategies in their cloud initiatives. © 2023 IEEE.

关键词： Forecasting

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Machine Learning Integration in Supplier Relationship Management for Enhanced Stock Management

Machine Learning Integration in Supplier Relationship Manage...

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international conference on Optimization and Applications (ICOA)

作者： Ameur Najim Zoubida Benmamoun Mouad Ben Mamoun Faculty of Sciences ANISSE Mohammed V University in Rabat Rabat Morocco Faculty of Engineering Liwa College Abu Dhabi UAE

In today's modern age, technology has evolved in many aspects of day-to-day life, from the simplest task to the more complicated one. therefore, companies are relying on modern development which is based on softwa... 详细信息

ISBN: (数字)9798350387353

ISBN: (纸本)9798350387360

关键词： Data analysis Navigation Supply chains Process control Machine learning predictive models Risk management Optimization Monitoring software engineering

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Enhancement to test case prioritization through object-oriented software architectural design 10

Enhancement to test case prioritization through object-orien...

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10th international conference on software Process Improvement (CIMPS)

作者： Davila Nicanor, Leticia Orozco Aguirre, Hector Rafael Quintana Lopez, Maricela Banda Madrid, Abraham Univ Autonoma Estado Mexico Ctr Univ UAEM Valle Mexico Estado De Mexico Mexico

ISBN: (纸本)9781728195155

Numerous prioritization techniques have been developed taking into account time and cost constraints frequently encountered in software system development at the testing phase. theoretically, it is possible to establish random predictive models, however, to obtain more precise results, it is important to consider the real context characteristics and the system scope to be evaluated. the main objective of this proposal is to develop a deterministic test case prioritization model, based on the object-oriented software architectural design. In the analysis process, graph theory is used to abstract the system architecture weighted with software metrics. the obtained results show that this approach can establish predictive models with greater precision and profitability in a practical area.

关键词： Test Case Prioritization Architectural software Design software Metrics

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