Abstract

Abstract
Testing is a mandatory activity to guarantee software quality. Not only knowledge about the software under testing is required to generate high-quality test cases, but also knowledge about the business rules implemented in software product to cover more than 80% of its source code therefore, we investigate in this study the adequacy, effectiveness, and cost of smart and random automated generated test sets for Java programs. We observed that the smart generated test sets, in general, are more adequate and less expensive than random generated tests, but regarding effectiveness, random generated test are more efficient. Moreover, we observed that smart automated test sets are complementary between them, and we explored if random generated test sets could be complementary to smart automated test sets as well. When we combined smart generated test sets, we observed an increase of more than 8% in statement coverage and more than 15% in mutation score when compared to random generated test sets. However, when we added random generated test sets to previous combination of smart generated test sets, results show a lower increase of statement coverage and mutation score, while increasing considerably the test set generation cost therefore, we advocate that the use of random testing should be integrated with smart generated tests only with a minimization strategy to avoid redundant test sets, keeping the cost reasonable.

Abstract
Notwithstanding its disruptive potential, which has been the object of considerable debate, Industry4.0 (I4.0) operationalisation still needs significant study. Specifically, scheduling is a key process that should be explored from this perspective. The purpose of this study is to shed light on the issues regarding scheduling that need to be considered in the new I4.0 framework. To achieve this, a two-stage cascade literature review is performed. The review begins with an analysis regarding the opportunities and challenges brought by I4.0 to the scheduling field, outputting a set of critical scheduling areas (CSA) in which development is essential. The second-stage literature review is performed to understand which steps have been taken so far by previous research in the scheduling field to address those challenges. Thus, a first contribution of this work is to provide insight on the influence and expected changes brought by I4.0 to scheduling, while showcasing relevant research. Another contribution is to identify the most promising future lines of research in this field, in which relevant challenges such as holistic scheduling, or increased flexibility requirements are highlighted. Concurrently, CSA such as decentralised decision-making, and human-robot collaboration display large gaps between current practice and the required technological level of development.

Abstract
We define a dynamic logic for QASM (Quantum Assembly) programming language, a language that requires the handling of quantum and probabilistic information. We provide a syntax and a model to this logic, providing a probabilistic semantics to the classical part. We exercise it with the quantum coin toss program.

Abstract
We study a simple inductive data type for representing correct-by-construction matrices. Despite its simplicity, it can be used to implement matrix-manipulation algorithms efficiently and safely, performing in some cases faster than existing alternatives even though the algorithms are written in a direct and purely functional style. A rich collection of laws makes it possible to derive and optimise these algorithms using equational reasoning, avoiding the notorious off-by-one indexing errors when fiddling with matrix dimensions. We demonstrate the usefulness of the data type on several examples, and highlight connections to related topics in category theory. © 2020 ACM.

Abstract
Nowadays an increasing number of vehicles are being equipped with powerful cockpit systems capable of collecting drivers’ footprints over time. The collection of this valuable data opens effective opportunities for routine prediction. With the growing ability of vehicles to collect spatial and temporal information solving the routine prediction problem becomes crucial and feasible. It is then extremely important to advance and take advantage of the capabilities of these cockpit systems. A vehicle that is capable of predicting the next destination of the driver and when the driver intends to leave to that destination can prepare the journey in advance. Previous studies tackling the next location prediction problem have made use of Traditional Markov models, Neural Networks, Dynamic models, among others. In this work, a framework based on the hierarchical density-based clustering algorithm followed by a Long Short-Term Memory (LSTM) recurrent neural network is proposed for spatial-temporal prediction of drivers’ routines. Based on real-life driving scenarios of three different users, the proposed approach achieved a test set accuracy of 96.20%, 90.23%, and 86.40% when predicting the next destination and a Score of 93.69, 79.21, and 28.81 when predicting the departure time, respectively. The results indicate that the proposed architecture can be implemented on the vehicle cockpit for the assistance of the management of future trips. © 2020, Springer Nature Switzerland AG.