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Many code smell detection techniques and tools have been proposed, mainly aiming to eliminate design flaws and improve software quality. Most of them are based on heuristics which rely on a set of software metrics and corresponding threshold values. Those techniques and tools suffer from subjectivity issues, discordant results among the tools, and the reliability of the thresholds. To mitigate these problems, we used machine learning to automate developers’ perception in code smells detection. Different from other existing machine learning used in code smell detection we trained our models with an extensive dataset based on more than 3000 professional reviews on 518 open source projects. We conclude by an empirical evaluation of the performance of the machine learning approach against PMD, a widely used metric-based code smell detection tool for Java. The experimental results show that the machine learning approach outperforms the PMD classifier in all evaluations.

This book contains a selection of the best papers of the 32nd Benelux Conference on Artificial Intelligence, BNAIC/Benelearn 2020, held in Leiden, The Netherlands, in November 2020. Due to the COVID-19 pandemic the conference was held online. The 12 papers presented in this volume were carefully reviewed and selected from 41 regular submissions. They address various aspects of artificial intelligence such as natural language processing, agent technology, game theory, problem solving, machine learning, human-agent interaction, AI and education, and data analysis.

Food identification technology potentially benefits both food and media industries, and can enrich human-computer interaction. We assembled a food classification dataset consisting of 11,141 clips, based on YouTube videos of 20 food types. This dataset is freely available on Kaggle. We suggest the grouped holdout evaluation protocol as evaluation method to assess model performance. As a first approach, we applied Convolutional Neural Networks on this dataset. When applying an evaluation protocol based on grouped holdout, the model obtained an accuracy of 18.5%, whereas when applying an evaluation protocol based on uniform holdout, the model obtained an accuracy of 37.58%. When approaching this as a binary classification task, the model performed well for most pairs. In both settings, the method clearly outperformed reasonable baselines. We found that besides texture properties, eating action differences are important consideration for data driven eating sound researches. Protocols based on biting sound are limited to textural classification and less heuristic while assembling food differences.

© The Author(s) 2019. The goal of quantitative structure activity relationship (QSAR) learning is to learn a function that, given the structure of a small molecule (a potential drug), outputs the predicted activity of the compound. We employed multi-task learning (MTL) to exploit commonalities in drug targets and assays. We used datasets containing curated records about the activity of specific compounds on drug targets provided by ChEMBL. Totally, 1091 assays have been analysed. As a baseline, a single task learning approach that trains random forest to predict drug activity for each drug target individually was considered. We then carried out feature-based and instance-based MTL to predict drug activities. We introduced a natural metric of evolutionary distance between drug targets as a measure of tasks relatedness. Instance-based MTL significantly outperformed both, feature-based MTL and the base learner, on 741 drug targets out of 1091. Feature-based MTL won on 179 occasions and the base learner performed best on 171 drug targets. We conclude that MTL QSAR is improved by incorporating the evolutionary distance between targets. These results indicate that QSAR learning can be performed effectively, even if little data is available for specific drug targets, by leveraging what is known about similar drug targets. This research was funded by the Engineering and Physical Sciences Research Council (EPSRC) grant EP/K030469/1. NS would like to thank the EU PhenoM-eNal project (Horizon 2020, 654241)

Algorithm selection methods can be speeded-up substantially by incorporating multi-objective measures that give preference to algorithms that are both promising and fast to evaluate. In this paper, we introduce such a measure, A3R, and incorporate it into two algorithm selection techniques: average ranking and active testing. Average ranking combines algorithm rankings observed on prior datasets to identify the best algorithms for a new dataset. The aim of the second method is to iteratively select algorithms to be tested on the new dataset, learning from each new evaluation to intelligently select the next best candidate. We show how both methods can be upgraded to incorporate a multi-objective measure A3R that combines accuracy and runtime. It is necessary to establish the correct balance between accuracy and runtime, as otherwise time will be wasted by conducting less informative tests. The correct balance can be set by an appropriate parameter setting within function A3R that trades off accuracy and runtime. Our results demonstrate that the upgraded versions of Average Ranking and Active Testing lead to much better mean interval loss values than their accuracy-based counterparts.

The algorithm selection problem is to choose the most suitable algorithm for solving a given problem instance. It leverages the complementarity between different approaches that is present in many areas of AI. We report on the state of the art in algorithm selection, as defined by the Algorithm Selection competitions in 2015 and 2017. The results of these competitions show how the state of the art improved over the years. We show that although performance in some cases is very good, there is still room for improvement in other cases. Finally, we provide insights into why some scenarios are hard, and pose challenges to the community on how to advance the current state of the art.

Ensembles of classifiers are among the best performing classifiers available in many data mining applications, including the mining of data streams. Rather than training one classifier, multiple classifiers are trained, and their predictions are combined according to a given voting schedule. An important prerequisite for ensembles to be successful is that the individual models are diverse. One way to vastly increase the diversity among the models is to build an heterogeneous ensemble, comprised of fundamentally different model types. However, most ensembles developed specifically for the dynamic data stream setting rely on only one type of base-level classifier, most often Hoeffding Trees. We study the use of heterogeneous ensembles for data streams. We introduce the Online Performance Estimation framework, which dynamically weights the votes of individual classifiers in an ensemble. Using an internal evaluation on recent training data, it measures how well ensemble members performed on this and dynamically updates their weights. Experiments over a wide range of data streams show performance that is competitive with state of the art ensemble techniques, including Online Bagging and Leveraging Bagging, while being significantly faster. All experimental results from this work are easily reproducible and publicly available online.

A basic step for each data-mining or machine learning task is to determine which model to choose based on the problem and the data at hand. In this paper we investigate when non-linear classifiers outperform linear classifiers by means of a large scale experiment. We benchmark linear and non-linear versions of three types of classifiers (support vector machines; neural networks; and decision trees), and analyze the results to determine on what type of datasets the non-linear version performs better. To the best of our knowledge, this work is the first principled and large scale attempt to support the common assumption that non-linear classifiers excel only when large amounts of data are available.

With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used in data mining. However, this progress is not yet matched by equal progress on automatic analyses that yield information beyond performance-optimizing hyperparameter settings. In this work, we aim to answer the following two questions: Given an algorithm, what are generally its most important hyperparameters, and what are typically good values for these? We present methodology and a framework to answer these questions based on meta-learning across many datasets. We apply this methodology using the experimental meta-data available on OpenML to determine the most important hyperparameters of support vector machines, random forests and Adaboost, and to infer priors for all their hyperparameters. The results, obtained fully automatically, provide a quantitative basis to focus efforts in both manual algorithm design and in automated hyperparameter optimization. The conducted experiments confirm that the hyperparameters selected by the proposed method are indeed the most important ones and that the obtained priors also lead to statistically significant improvements in hyperparameter optimization., Comment: \c{opyright} 2018. Copyright is held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use, not for redistribution. The definitive Version of Record was published in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

It is often claimed that data pre-processing is an important factor contributing towards the performance of classification algorithms. In this paper we investigate feature selection, a common data pre-processing technique. We conduct a large scale experiment and present results on what algorithms and data sets benefit from this technique. Using meta-learning we can find out for which combinations this is the case. To complement a large set of meta-features, we introduce the Feature Selection Landmarkers, which prove useful for this task. All our experimental results are made publicly available on OpenML.

Many sciences have made significant breakthroughs by adopting online tools that help organize, structure and mine information that is too detailed to be printed in journals. In this paper, we introduce OpenML, a place for machine learning researchers to share and organize data in fine detail, so that they can work more effectively, be more visible, and collaborate with others to tackle harder problems. We discuss how OpenML relates to other examples of networked science and what benefits it brings for machine learning research, individual scientists, as well as students and practitioners., Comment: 12 pages, 10 figures