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Artificial intelligence to automate the systematic review of scientific literature

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arXiv 2024年

作者： de la Torre-López, José Ramírez, Aurora Romero, José Raúl Dept. Computer Science and Numerical Analysis University of Córdoba Rabanales Campus Córdoba14071 Spain

Artificial intelligence (AI) has acquired notorious relevance in modern computing as it effectively solves complex tasks traditionally done by humans. AI provides methods to represent and infer knowledge, efficiently manipulate texts and learn from vast amount of data. These characteristics are applicable in many activities that human find laborious or repetitive, as is the case of the analysis of scientific literature. Manually preparing and writing a systematic literature review (SLR) takes considerable time and effort, since it requires planning a strategy, conducting the literature search and analysis, and reporting the findings. Depending on the area under study, the number of papers retrieved can be of hundreds or thousands, meaning that filtering those relevant ones and extracting the key information becomes a costly and error-prone process. However, some of the involved tasks are repetitive and, therefore, subject to automation by means of AI. In this paper, we present a survey of AI techniques proposed in the last 15 years to help researchers conduct systematic analyses of scientific literature. We describe the tasks currently supported, the types of algorithms applied, and available tools proposed in 34 primary studies. This survey also provides a historical perspective of the evolution of the field and the role that humans can play in an increasingly automated SLR *** Codes 68T01 © 2024, CC BY.

关键词： Machine learning

Towards Explainable Test Case Prioritisation with Learning-to-Rank Models

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arXiv 2024年

作者： Ramírez, Aurora Berrios, Mario Romero, José Raúl Feldt, Robert Dept. of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain Softw. Eng. Division Dept. of Computer Science and Engineering Chalmers University of Technology Goteborg Sweden

Test case prioritisation (TCP) is a critical task in regression testing to ensure quality as software evolves. Machine learning has become a common way to achieve it. In particular, learning-to-rank (LTR) algorithms provide an effective method of ordering and prioritising test cases. However, their use poses a challenge in terms of explainability, both globally at the model level and locally for particular results. Here, we present and discuss scenarios that require different explanations and how the particularities of TCP (multiple builds over time, test case and test suite variations, etc.) could influence them. We include a preliminary experiment to analyse the similarity of explanations, showing that they do not only vary depending on test case-specific predictions, but also on the relative ranks. © 2024, CC BY-NC-ND.

关键词： Machine learning

Predictive Maintenance with Large Language Models and Transformer-based Survival analysis

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Predictive Maintenance with Large Language Models and Transf...

Annual Conference of Industrial Electronics Society

作者： Aurora Esteban Victor Cobilean Rashmika Nawaratne Dept. of Computer Science and Numerical Analysis University of Cordoba Cordoba Spain Dept. of Computer Science Virginia Commonwealth University Richmond VA USA Research Center for Data Analytics and Cognition La Trobe University Victoria Australia

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

关键词： Analytical models Schedules Large language models Virtual assistants Natural languages Predictive models Transformers Maintenance Monitoring Predictive maintenance

Convolutional and Deep Learning based techniques for Time Series Ordinal Classification

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arXiv 2023年

作者： Ayllón-Gavilán, Rafael Guijo-Rubio, David Gutiérrez, Pedro Antonio Bagnall, Anthony Hervás-Martínez, César Dept. of Clinical-Epidemiological Research in Primary Care IMIBIC Spain Dept. of Computer Science and Numerical Analysis University of Cordoba Spain Dept. of Electronics and Computer Science University of Southampton United Kingdom

Time Series Classification (TSC) covers the supervised learning problem where input data is provided in the form of series of values observed through repeated measurements over time, and whose objective is to predict the category to which they belong. When the class values are ordinal, classifiers that take this into account can perform better than nominal classifiers. Time Series Ordinal Classification (TSOC) is the field bridging this gap, yet unexplored in the literature. There are a wide range of time series problems showing an ordered label structure, and TSC techniques that ignore the order relationship discard useful information. Hence, this paper presents the first benchmarking of TSOC methodologies, exploiting the ordering of the target labels to boost the performance of current TSC state-of-the-art. Both convolutional- and deep learning-based methodologies (among the best performing alternatives for nominal TSC) are adapted for TSOC. For the experiments, a selection of 29 ordinal problems has been made. In this way, this paper contributes to the establishment of the state-of-the-art in TSOC. The results obtained by ordinal versions are found to be significantly better than current nominal TSC techniques in terms of ordinal performance metrics, outlining the importance of considering the ordering of the labels when dealing with this kind of problems. © 2023, CC BY-NC-ND.

关键词： Convolution

Towards Explainable Test Case Prioritisation with Learning-to-Rank Models

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Towards Explainable Test Case Prioritisation with Learning-t...

IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW

作者： Aurora Ramírez Mario Berrios José Raúl Romero Robert Feldt Dept. of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain Dept. of Computer Science and Engineering Softw. Eng. Division Chalmers University of Technology Goteborg Sweden

关键词： Software testing Machine learning algorithms Conferences Software algorithms Machine learning Software Task analysis

Automated Machine Learning for Test Case Prioritization

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SSRN

SSRN 2023年

作者： Romero, José Raúl Ramírez, Aurora García-Martínez, Carlos Dept. of Computer Science and Numerical Analysis University of Córdoba AN Córdoba14071 Spain Andalusian Research Institute in Data Science and Computational Intelligence University of Córdoba AN Córdoba14071 Spain

Test case prioritization (TCP) involves ordering and selecting the most relevant test cases to verify that the current functionality of a software system remains unaffected by code changes. Recently, TCP has been solved by machine learning (ML), predicting the failure probability of each test case. However, software engineers may struggle to identify and implement the most suitable predictive models for TCP. As new builds adapt the test suite being tested, the model performance may decline as these new builds are incorporated. In this study, we address these challenges by applying automated workflow composition and hyperparameter optimization. Both are considered tasks within automatic machine learning (AutoML). With this aim, our proposal employs grammar-guide genetic programming as the underlying mechanism for implementing the AutoML algorithm. Our experimental results demonstrate that our approach can adapt to the particularities of the system under test, selecting the most appropriate workflow and hyperparameters for each build. More importantly, our approach eliminates the need for testers to possess extensive knowledge of ML, while allowing them to inspect workflows to understand the evolution of the prioritization process. This research showcases the potential of AutoML in software engineering, specifically for the TCP problem. © 2023, The Authors. All rights reserved.

关键词： Genetic programming

Tree-Shaped Ensemble of Multi-Label Classifiers using Grammar-Guided Genetic Programming

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Tree-Shaped Ensemble of Multi-Label Classifiers using Gramma...

Congress on Evolutionary Computation

作者： Jose M. Moyano Eva L. Gibaja Krzysztof J. Cios Sebastián Ventura Dept. of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain Dept. of Computer Science Virginia Commonwealth University Richmond VA USA

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

Multi-label classification paradigm has had a growing interest because of the emergence of a large number of classification problems where each of the instances of the data can be associated with several output labels simultaneously. Several ensemble methods were proposed to solve the multilabel classification problem. However, most of them simply create diversity in the ensemble by following a random procedure and give the same importance to all members. In this paper, we propose a Grammar-Guided Genetic Programming algorithm to build ensembles of multi-label classifiers. Given a pool of multilabel classifiers, each of them modeling dependencies among a subset of k labels, they are combined into a tree-shaped ensemble. At each node of the tree, predictions of its children nodes are combined, while each leaf represents a classifier from the pool. We propose two configurations for the method: using a fixed value of k for all classifiers in the pool, or using a variable value of k for each classifier, thus being able to capture relationships among groups of labels of different size in the ensemble. The experiments performed over sixteen multi-label dataset and using five evaluation metrics demonstrated that our method performs significantly better than the state-of-the-art ensembles of multilabel classifiers.

关键词： Prediction algorithms Grammar Vegetation Genetic programming Sociology Statistics

Time series ordinal classification via shapelets

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Time series ordinal classification via shapelets

International Joint Conference on Neural Networks (IJCNN)

作者： David Guijo-Rubio Pedro A. Gutiérrez Anthony Bagnall César Hervás-Martínez Dept. of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain School of Computing Sciences University of East Anglia Norwich United Kingdom

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Nominal time series classification has been widely developed over the last years. However, to the best of our knowledge, ordinal classification of time series is an unexplored field, and this paper proposes a first approach in the context of the shapelet transform (ST). For those time series dataset where there is a natural order between the labels and the number of classes is higher than 2, nominal classifiers are not capable of achieving the best results, because the models impose the same cost of misclassification to all the errors, regardless the difference between the predicted and the ground-truth. In this sense, we consider four different evaluation metrics to do so, three of them of an ordinal nature. The first one is the widely known Information Gain (IG), proved to be very competitive for ST methods, whereas the remaining three measures try to boost the order information by refining the quality measure. These three measures are a reformulation of the Fisher score, the Spearman's correlation coefficient (ρ), and finally, the Pearson's correlation coefficient (R 2 ). An empirical evaluation is carried out, considering 7 ordinal datasets from the UEA & UCR time series classification repository, 4 classifiers (2 of them of nominal nature, whereas the other 2 are of ordinal nature) and 2 performance measures (correct classification rate, CCR, and average mean absolute error, AMAE). The results show that, for both performance metrics, the ST quality metric based on R 2 is able to obtain the best results, specially for AMAE, for which the differences are statistically significant in favour of R 2 .

关键词： Time series analysis Shape measurement Time measurement Correlation computer science numerical analysis

Chaos suppression in a gompertz-like discrete system of fractional order

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arXiv 2019年

作者： Danca, Marius-F Fečkan, Michal Dept. of Mathematics and Computer Science Avram Iancu University of Cluj-Napoca Romania Romanian Institute od Science and Technology Cluj-Napoca400487 Romania Dept. of Mathematical Analysis and Numerical Mathematics Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Slovak Republic Mathematical Institute Slovak Academy of Sciences Slovakia

In this paper we introduce the fractional-order variant of a Gompertz-like discrete system. The chaotic behavior is suppressed with an impulsive control algorithm. The numerical integration and the Lyapunov exponent are obtained by means of the discrete fractional calculus. To verify numerically the obtained results, beside the Lyapunov exponent, the tools offered by the 0-1 test are used. Copyright © 2019, The Authors. All rights reserved.

关键词： Calculations