Tools solving the program synthesis problem often take as input partial specification, whether in the form of logical formulas, types or examples. Human users tend to specify just enough to explain the needed solution to themselves, and thus expose the gap between human thought, which is capable of generalization from the partial specifications, and synthesis tools and their backend ML models which are only able to generalize once the form of bias has been selected for them.

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Emerging Industrial Internet of Things (iIoT) platforms generate cross-company added value, providing functionalities and technologies for a variety of digital services in the industrial engineering. iIoT platforms integrate various stakeholders, such as end customers and complementors and build iIoT ecosystems. Earlier research has recognized boundary resources as an emergence and governance mechanism for software ecosystems. In this study we apply the boundary resources for iIoT by exploring the longitudinal case study of the Siemens MindSphere ecosystem. The goal of this exploratory paper is to show which boundary resources are currently used in iIoT ecosystems and how do they impact the development of iIoT ecosystems.

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An oracle is used in software testing to derive the verdict (pass/fail) for a test case. Lack of precise test oracles is one of the major problems in software testing which can hinder judgements about quality. Metamorphic testing is an emerging technique which solves both the oracle problem and the test case generation problem by testing special forms of software requirements known as metamorphic requirements. However, manually deriving the metamorphic requirements for a given program requires a high level of domain expertise, is labor intensive and error prone. As an alternative, we consider the problem of automatic detection of metamorphic requirements using machine learning (ML). For this problem we can apply graph kernels and support vector machines (SVM). A significant problem for any ML approach is to obtain a large labeled training set of data (in this case programs) that generalises well. The main contribution of this paper is a general method to generate large volumes of synthetic training data which can improve ML assisted detection of metamorphic requirements. For training data synthesis we adopt mutation testing techniques. This research is the first to explore the area of data augmentation techniques for ML-based analysis of software code. We also have the goal to enhance black-box testing using white-box methodologies. Our results show that the mutants incorporated into the source code corpus not only efficiently scale the dataset size, but they can also improve the accuracy of classification models.

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Code smells represent a well-known problem in software engineering, since they are a notorious cause of loss of comprehensibility and maintainability. The most recent efforts in devising automatic machine learning-based code smell detection techniques have achieved unsatisfying results so far. This could be explained by the fact that all these approaches follow a within-project classification, i.e. training and test data are taken from the same source project, which combined with the imbalanced nature of the problem, produces datasets with a very low number of instances belonging to the minority class (i.e. smelly instances). In this paper, we propose a cross-project machine learning approach and compare its performance with a within-project alternative. The core idea is to use transfer learning to increase the overall number of smelly instances in the training datasets. Our results have shown that cross-project classification provides very similar performance with respect to within-project. Despite this finding does not yet provide a step forward in increasing the performance of ML techniques for code smell detection, it sets the basis for further investigations.

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Metamorphic testing is a popular testing technique that has shown to be effective at detecting faults in numerous domains such as web services and autonomous vehicles. Despite the many advances made in the last two decades, however, metamorphic testing is still a fertile soil for new contributions. This talk will provide an overview of the current state of the discipline and some of the key challenges to be addressed from three different perspectives: the technique, its applications, and the research community. The speech and the subsequent discussion aims to provide the audience with a common view of the field and the work to be done, paving the way for new promising contributions.

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