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Software testing is key for quality assurance of embedded systems. However, with increased development pace, the amount of test results data risks growing to a level where exploration and visualization of the results are unmanageable. This paper covers a tool, Tim, implemented at a company developing embedded systems, where software development occurs in parallel branches and nightly testing is partitioned over software branches, test systems and test cases. Tim aims to replace a previous solution with problems of scalability, requirements and technological flora. Tim was implemented with a reference group over several months. For validation, data were collected both from reference group meetings and logs from the usage of the tool. Data were analyzed quantitatively and qualitatively. The main contributions from the study include the implementation of eight views for test results exploration and visualization, the identification of four solutions patterns for these views (filtering, aggregation, previews and comparisons), as well as six challenges frequently discussed at reference group meetings (expectations, anomalies, navigation, integrations, hardware details and plots). Results are put in perspective with related work and future work is proposed, e.g., enhanced anomaly detection and integrations with more systems such as risk management, source code and requirements repositories. [ABSTRACT FROM AUTHOR]

A major challenge for the European electronic industry is to enhance productivity by ensuring quality of development, integration and maintenance while reducing the associated costs. Model-Driven Engineering (MDE) principles and techniques have already shown promising capabilities, but they still need to scale up to support real-world scenarios implied by the full deployment and use of complex electronic components and systems. Moreover, maintaining efficient traceability, integration, and communication between two fundamental system life cycle phases (design time and runtime) is another challenge requiring the scalability of MDE. This paper presents an overview of the ECSEL 1 1 http://www.ecsel-ju.eu/web/index.php . project entitled “MegaModelling at runtime – Scalable model-based framework for continuous development and runtime validation of complex systems” (MegaM@Rt2), whose aim is to address the above mentioned challenges facing MDE. Driven by both large and small industrial enterprises, with the support of research partners and technology providers, MegaM@Rt2 aims to deliver a framework of tools and methods for: 1) system engineering/design and continuous development, 2) related runtime analysis and 3) global models and traceability management. Diverse industrial use cases (covering strategic domains such as aeronautics, railway, construction and telecommunications) will integrate and demonstrate the validity of the MegaM@Rt2 solution. This paper provides an overview of the MegaM@Rt2 project with respect to its approach, mission, objectives as well as to its implementation details. It further introduces the consortium as well as describes the work packages and few already produced deliverables. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this paper is to investigate the evidence for symbolic regression using genetic programming (GP) being an effective method for prediction and estimation in software engineering, when compared with regression/machine learning models and other comparison groups (including comparisons with different improvements over the standard GP algorithm). We performed a systematic review of literature that compared genetic programming models with comparative techniques based on different independent project variables. A total of 23 primary studies were obtained after searching different information sources in the time span 1995–2008. The results of the review show that symbolic regression using genetic programming has been applied in three domains within software engineering predictive modeling: (i) Software quality classification (eight primary studies). (ii) Software cost/effort/size estimation (seven primary studies). (iii) Software fault prediction/software reliability growth modeling (eight primary studies). While there is evidence in support of using genetic programming for software quality classification, software fault prediction and software reliability growth modeling; the results are inconclusive for software cost/effort/size estimation. [Copyright &y& Elsevier]

MegaM@Rt2 is a large European project dedicated to the provisioning of a model-based methodology and supporting tooling for system engineering at a wide scale. It notably targets the continuous development and runtime validation of such complex systems by developing a framework addressing a large set of engineering processes and application domains. This collaborative project involves 27 partners from 6 different countries, 9 industrial case studies as well as over 30 different software tools from project partners (and others). In the context of the MegaM@Rt2 project, we elaborated on a pragmatic model-driven approach to specify the case study requirements, design the high-level architecture of a framework, perform the gap analysis between the industrial needs and current state-of-the-art, and plan a first framework development roadmap accordingly. The present paper describes the generic tool-supported approach that came out as a result. It also details its concrete application in the MegaM@Rt2 project. In particular, we discuss the collaborative modeling process, the requirement definition tooling, the approach for components modeling, as well as the traceability and document generation. In addition, we show how we used the proposed solution to specify the MegaM@Rt2 framework's conceptual tool components centered around three complementary tool sets: the MegaM@Rt2 System Engineering Tool Set, the MegaM@Rt2 Runtime Analysis Tool Set and the MegaM@Rt2 Model & Traceability Management Tool Set. The paper ends with a discussion on the practical lessons we have learned from this work so far. [ABSTRACT FROM AUTHOR]

Hyper-heuristic is a new methodology for the adaptive hybridization of meta-heuristic algorithms to derive a general algorithm for solving optimization problems. This work focuses on the selection type of hyper-heuristic, called the exponential Monte Carlo with counter (EMCQ). Current implementations rely on the memory-less selection that can be counterproductive as the selected search operator may not (historically) be the best performing operator for the current search instance. Addressing this issue, we propose to integrate the memory into EMCQ for combinatorial t-wise test suite generation using reinforcement learning based on the Q-learning mechanism, called Q-EMCQ. The limited application of combinatorial test generation on industrial programs can impact the use of such techniques as Q-EMCQ. Thus, there is a need to evaluate this kind of approach against relevant industrial software, with a purpose to show the degree of interaction required to cover the code as well as finding faults. We applied Q-EMCQ on 37 real-world industrial programs written in Function Block Diagram (FBD) language, which is used for developing a train control management system at Bombardier Transportation Sweden AB. The results show that Q-EMCQ is an efficient technique for test case generation. Addition- ally, unlike the t-wise test suite generation, which deals with the minimization problem, we have also subjected Q-EMCQ to a maximization problem involving the general module clustering to demonstrate the effectiveness of our approach. The results show the Q-EMCQ is also capable of outperforming the original EMCQ as well as several recent meta/hyper-heuristic including modified choice function, Tabu high-level hyper-heuristic, teaching learning-based optimization, sine cosine algorithm, and symbiotic optimization search in clustering quality within comparable execution time. [ABSTRACT FROM AUTHOR]

Software systems are often built in increments with additional features or enhancements on top of existing products. This incremental development may result in the deterioration of certain quality aspects. In other words, the software can be considered an evolving entity emanating different quality characteristics as it gets updated over time with new features or deployed in different operational environments. Approaching software development with this mindset and awareness regarding quality evolution over time can be a key factor for the long-term success of a company in today's highly competitive market of industrial software-intensive products. Therefore, it is important to be able to accurately analyze and determine the quality implications of each change and increment to a software system. To address this challenge, the multinational SmartDelta project develops automated solutions for the quality assessment of product deltas in a continuous engineering environment. The project provides smart analytics from development artifacts and system executions, offering insights into quality degradation or improvements across different product versions, and providing recommendations for the next builds. This paper presents the challenges in incremental software development tackled in the scope of the SmartDelta project, and the solutions that are produced and planned in the project, along with the industrial impact of the project for software-intensive industrial systems. • The SmartDelta project on automated quality assurance for product deltas and the project consortium are presented. • Novel solutions and software tools resulting from the project are described. • Industrial impact and relevance of the project, and some future directions are outlined. [ABSTRACT FROM AUTHOR]

Requirements elicitation is one of the essential steps towards software design and construction. Business analysts and stakeholders often face challenges in gathering or conveying key software requirements. There are many methods and tools designed by researchers and practitioners but with the persistent development of new technologies, there is a need to make requirements gathering and design‐rationale process more efficient and adaptable. Storytelling is an emerging concept and researchers are witnessing its effectiveness in education, community building, information system, and requirement elicitation. Objectives of this study are to devise a method for requirements elicitation and improving design‐rationales using story‐based techniques and evaluate the effectiveness of the proposed activity. To answer the research objectives, the authors have conducted open‐ended interviews to get feedback on the proposed method; the authors have case requirement from a running project to map how this method can be useful; and performed empirical evaluation of the proposed card‐based activity. The estimated regression model, in our study, has shown that participants' perception about the simplicity/easiness and the joy of playing the game has an eventual positive effect on requirements elicitation through enhancing user's desire to play the game, which in turn increases the collaborative learning outcomes of the game. [ABSTRACT FROM AUTHOR]

Combinatorial test design is a plan of test that aims to reduce the amount of test cases systematically by choosing a subset of the test cases based on the combination of input variables. The subset covers all possible combinations of a given strength and hence tries to match the effectiveness of the exhaustive set. This mechanism of reduction has been used successfully in software testing research with t-way testing (where t indicates the interaction strength of combinations). Potentially, other systems may exhibit many similarities with this approach. Hence, it could form an emerging application in different areas of research due to its usefulness. To this end, more recently it has been applied in a few research areas successfully. In this paper, we explore the applicability of combinatorial test design technique for Fractional Order (FO), Proportional-Integral-Derivative (PID) parameter design controller, named as FOPID, for an automatic voltage regulator (AVR) system. Throughout the paper, we justify this new application theoretically and practically through simulations. In addition, we report on first experiments indicating its practical use in this field. We design different algorithms and adapted other strategies to cover all the combinations with an optimum and effective test set. Our findings indicate that combinatorial test design can find the combinations that lead to optimum design. Besides this, we also found that by increasing the strength of combination, we can approach to the optimum design in a way that with only 4-way combinatorial set, we can get the effectiveness of an exhaustive test set. This significantly reduced the number of tests needed and thus leads to an approach that optimizes design of parameters quickly. [ABSTRACT FROM AUTHOR]

Detecting the dependency between integration test cases plays a vital role in the area of software test optimization. Classifying test cases into two main classes – dependent and independent – can be employed for several test optimization purposes such as parallel test execution, test automation, test case selection and prioritization, and test suite reduction. This task can be seen as an imbalanced classification problem due to the test cases' distribution. Often the number of dependent and independent test cases is uneven, which is related to the testing level, testing environment and complexity of the system under test. In this study, we propose a novel methodology that consists of two main steps. Firstly, by using natural language processing we analyze the test cases' specifications and turn them into a numeric vector. Secondly, by using the obtained data vectors, we classify each test case into a dependent or an independent class. We carry out a supervised learning approach using different methods for handling imbalanced datasets. The feasibility and possible generalization of the proposed methodology is evaluated in two industrial projects at Bombardier Transportation, Sweden, which indicates promising results. • In a manual testing procedure, all testing artifacts are written in a natural text, employing natural language processing techniques might provide highly useful information for test optimization purposes. • The ratio of dependent and independent test cases might suffer from an imbalanced distribution due to the testing level and complexity of the system under test. • Doc2Vec proves to be a good tool when transforming the manual test cases into feature vectors. • IFROWANN performs well when splitting dependent and independent test cases as an imbalance learning algorithm. [ABSTRACT FROM AUTHOR]

Recent studies have shown that excessive online information disclosure is a major reason of privacy breach. It makes it easy for social engineers to gather information about their targets. The objective of this study is to gather user privacy concerns reported in the literature and categorize them into themes, then design a serious game covering the categorized privacy concerns and evaluate the educational effect of the game regarding dangers associated with excessive online information disclosure. We have conducted a literature review and extracted user privacy concerns reported in 109+ publications. Then we designed a serious game and empirically evaluated the game players awareness of dangers associated with excessive online information disclosure. We find that privacy awareness has a positive long-term impact on users online behavior in terms of controlled information sharing. However, social networking needs drive users to share information online, even knowing the potential risks. The proposed serious game shows positive effect in improving the privacy awareness of participants. [ABSTRACT FROM AUTHOR]