The presence of code and design smells can have a severe impact on the quality of a program. Consequently, their detection and correction have drawn the attention of both researchers and practitioners who have proposed various approaches to detect code and design smells in programs. However, none of these approaches handle the inherent uncertainty of the detection process. We propose a Bayesian approach to manage this uncertainty. First, we present a systematic process to convert existing state-of-the-art detection rules into a probabilistic model. We illustrate this process by generating a model to detect occurrences of the Blob antipattern. Second, we present results of the validation of the model: we built this model on two open-source programs, GanttProject v1.10.2 and Xerces v2.7.0, and measured its accuracy. Third, we compare our model with another approach to show that it returns the same candidate classes while ordering them to minimise the quality analysts' effort. Finally, we show that when past detection results are available, our model can be calibrated using machine learning techniques to offer an improved, context-specific detection.

A GQM-based Method and a Bayesian Approach for the Detection of Code and Design Smells

This paper is investigating if it is possible to predict source code quality based on static analysis and machine learning. The proposed approach includes a plugin in Eclipse, uses a combination of peer review/human rating, static code analysis, and classification methods. As training data, public data and student hand-ins in programming are used. Based on this training data, new and uninspected source code can be accurately classified as “well written” or “badly written”. This is a step towards feedback in an interactive environment without peer assessment.

/pdf/predicting-source-code-quality-with-static-analysis-and-4uov7kz8s9.pdf

Predicting Source Code Quality with Static Analysis and Machine Learning

In the field of technology, software is an essential driver of business and industry. Software undergoes changes due to maintenance activities initiated by bug fixing, improved documentation, and new requirements of users. In software, code smells are indicators of a system which may give maintenance problem in future. This paper evaluates six types of machine learning algorithms to predict change-proneness using code smells as predictors for various versions of four Java-coded applications. Two approaches are used: method 1-random undersampling is done before Feature selection; method 2-feature selection is done prior to random undersampling. This paper concludes that gene expression programming (GEP) gives maximum AUC value, whereas cascade correlation network (CCR), treeboost, and PNN\GRNN algorithms are among top algorithms to predict F-measure, precision, recall, and accuracy. Also, GOD and L_M code smells are good predictors of software change-proneness. Results show that method 1 outperforms method 2.

Evaluation of Machine Learning Approaches for Change-Proneness Prediction Using Code Smells

Quality in software is often understood as "execution according to design purpose" whereas security means that "software will not put data or computing systems at risk of unauthorized access." There seems to be a connection between these two aspects but, how do we integrate both of them in the robotics development cycle? In this article we introduce DevSecOps in Robotics, a set of best practices designed to help roboticists implant security deep in the heart of their development and operations processes. First, we briefly describe DevOps, introduce the value added with DevSecOps and describe and illustrate how these practices may be implemented in the robotics field. We finalize with a discussion on the relationship between security, quality and safety, open problems and future research questions.

DevSecOps in Robotics.

Despite the ubiquity of data science, we are far from rigorously understanding how coding in data science is performed. Even though the scientific literature has hinted at the iterative and explorative nature of data science coding, we need further empirical evidence to understand this practice and its workflows in detail. Such understanding is critical to recognise the needs of data scientists and, for instance, inform tooling support. To obtain a deeper understanding of the iterative and explorative nature of data science coding, we analysed 470 Jupyter notebooks publicly available in GitHub repositories. We focused on the extent to which data scientists transition between different types of data science activities, or steps (such as data preprocessing and modelling), as well as the frequency and co-occurrence of such transitions. For our analysis, we developed a dataset with the help of five data science experts, who manually annotated the data science steps for each code cell within the aforementioned 470 notebooks. Using the first-order Markov chain model, we extracted the transitions and analysed the transition probabilities between the different steps. In addition to providing deeper insights into the implementation practices of data science coding, our results provide evidence that the steps in a data science workflow are indeed iterative and reveal specific patterns. We also evaluated the use of the annotated dataset to train machine-learning classifiers to predict the data science step(s) of a given code cell. We investigate the representativeness of the classification by comparing the workflow analysis applied to (a) the predicted data set and (b) the data set labelled by experts, finding an F1-score of about 71% for the 10-class data science step prediction problem. 

/pdf/workflow-analysis-of-data-science-code-in-public-github-r5b5vzud.pdf

Workflow analysis of data science code in public GitHub repositories

This paper investigates the feasibility of a plugin facilitating identification of code quality. The proposed solution includes a distributed plugin that performs static code analysis as well as machine learning based classification, and is able to identify well written and badly written code.

/pdf/towards-a-collaborative-code-review-plugin-emti2qrg1w.pdf

Towards a Collaborative Code Review Plugin

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Predicting Source Code Quality with Static Analysis and Machine Learning

Towards a Collaborative Code Review Plugin