Requirements Engineering (RE) is the initial step towards building a software system. The success or failure of a software project is firmly tied to this phase, based on communication among stakeholders using natural language. The problem with natura
l language is that it can easily lead to different understandings if it is not expressed precisely by the stakeholders involved, which results in building a product different from the expected one. Previous work proposed to enhance the quality of the software requirements detecting language errors based on ISO 29148 requirements language criteria. The existing solutions apply classical Natural Language Processing (NLP) to detect them. NLP has some limitations, such as domain dependability which results in poor generalization capability. Therefore, this work aims to improve the previous work by creating a manually labeled dataset and using ensemble learning, Deep Learning (DL), and techniques such as word embeddings and transfer learning to overcome the generalization problem that is tied with classical NLP and improve precision and recall metrics using a manually labeled dataset. The current findings show that the dataset is unbalanced and which class examples should be added more. It is tempting to train algorithms even if the dataset is not considerably representative. Whence, the results show that models are overfitting; in Machine Learning this issue is solved by adding more instances to the dataset, improving label quality, removing noise, and reducing the learning algorithms complexity, which is planned for this research.
We present a derivation-based Atiyah sequence for noncommutative principal bundles. Along the way we treat the problem of deciding when a given *-automorphism on the quantum base space lifts to a *-automorphism on the quantum total space that commutes with the underlying structure group.
We propose a new approach to increase inference performance in environments that require a specific sequence of actions in order to be solved. This is for example the case for maze environments where ideally an optimal path is determined. Instead of
learning a policy for a single step, we want to learn a policy that can predict n actions in advance. Our proposed method called policy horizon regression (PHR) uses knowledge of the environment sampled by A2C to learn an n dimensional policy vector in a policy distillation setup which yields n sequential actions per observation. We test our method on the MiniGrid and Pong environments and show drastic speedup during inference time by successfully predicting sequences of actions on a single observation.
The innovations of vehicle connectivity have been increasing dramatically to enhance the safety and user experience of driving, while the rising numbers of interfaces to the external world also bring security threats to vehicles. Many security counte
rmeasures have been proposed and discussed to protect the systems and services against attacks. To provide an overview of the current states in this research field, we conducted a systematic mapping study on the topic area security countermeasures of in-vehicle communication systems. 279 papers are identified based on the defined study identification strategy and criteria. We discussed four research questions related to the security countermeasures, validation methods, publication patterns, and research trends and gaps based on the extracted and classified data. Finally, we evaluated the validity threats, the study identification results, and the whole mapping process. We found that the studies in this topic area are increasing rapidly in recent years. However, there are still gaps in various subtopics like automotive Ethernet security, anomaly reaction, and so on. This study reviews the target field not only related to research findings but also research activities, which can help identify research gaps at a high level and inspire new ideas for future work.
Graphene is a material with enormous potential for numerous applications. Therefore, significant efforts are dedicated to large-scale graphene production using a chemical vapor deposition (CVD) technique. In addition, research is directed at developi
ng methods to incorporate graphene in established production technologies and process flows. In this paper, we present a brief review of available CVD methods for graphene synthesis. We also discuss scalable methods to transfer graphene onto desired substrates. Finally, we discuss potential applications that would benefit from a fully scaled, semiconductor technology compatible production process.
Mutation testing is used to evaluate the effectiveness of test suites. In recent years, a promising variation called extreme mutation testing emerged that is computationally less expensive. It identifies methods where their functionality can be entir
ely removed, and the test suite would not notice it, despite having coverage. These methods are called pseudo-tested. In this paper, we compare the execution and analysis times for traditional and extreme mutation testing and discuss what they mean in practice. We look at how extreme mutation testing impacts current software development practices and discuss open challenges that need to be addressed to foster industry adoption. For that, we conducted an industrial case study consisting of running traditional and extreme mutation testing in a large software project from the semiconductor industry that is covered by a test suite of more than 11,000 unit tests. In addition to that, we did a qualitative analysis of 25 pseudo-tested methods and interviewed two experienced developers to see how they write unit tests and gathered opinions on how useful the findings of extreme mutation testing are. Our results include execution times, scores, numbers of executed tests and mutators, reasons why methods are pseudo-tested, and an interview summary. We conclude that the shorter execution and analysis times are well noticeable in practice and show that extreme mutation testing supplements writing unit tests in conjunction with code coverage tools. We propose that pseudo-tested code should be highlighted in code coverage reports and that extreme mutation testing should be performed when writing unit tests rather than in a decoupled session. Future research should investigate how to perform extreme mutation testing while writing unit tests such that the results are available fast enough but still meaningful.
The unique properties and atomic thickness of two-dimensional (2D) materials enable smaller and better nanoelectromechanical sensors with novel functionalities. During the last decade, many studies have successfully shown the feasibility of using sus
pended membranes of 2D materials in pressure sensors, microphones, accelerometers, and mass and gas sensors. In this review, we explain the different sensing concepts and give an overview of the relevant material properties, fabrication routes, and device operation principles. Finally, we discuss sensor readout and integration methods and provide comparisons against the state of the art to show both the challenges and promises of 2D material-based nanoelectromechanical sensing.
The X-ray spectrum of extreme HBL type blazars is located in the synchrotron branch of the broadband spectral energy distribution (SED), at energies below the peak. A joint fit of the extrapolated X-ray spectra together with a host galaxy template al
lows characterizing the synchrotron branch in the SED. The X-ray spectrum is usually characterized either with a pure or a curved power-law model. In the latter case, however, it is hard to distinguish an intrinsic curvature from excess absorption. In this paper, we focus on five well-observed blazars: 1ES 0229+200, PKS 0548-322, RX J1136+6737, 1ES 1741+196, 1ES 2344+514. We constrain the infrared-to-X-ray emission of these five blazars using a model that is characterized by the host galaxy, spectral curvature, absorption, and ultraviolet excess to separate these spectral features. In the case of four sources: 1ES 0229+200, PKS 0548-322, 1ES 1741+196, 1ES 2344+514 the spectral fit with the atomic neutral hydrogen from the Leiden Argentina Bonn Survey result in a significant UV excess present in the broadband spectral energy distribution. Such excess can be interpreted as an additional component, for example, a blue bump. However, in order to describe spectra of these blazars without such excess, additional absorption to the atomic neutral hydrogen from the Leiden Argentina Bonn Survey is needed.
Motivation: How immature teams can become agile is a question that puzzles practitioners and researchers alike. Scrum is one method that supports agile working. Empirical research on the Scrum Master role remains scarce and reveals contradicting resu
lts. While the Scrum Master role is often centred on one person in rather immature teams, the role is expected to be shared among multiple members in mature teams. Objective: Therefore, we aim to understand how the Scrum Master role changes while the team matures. Method: We applied Grounded Theory and conducted qualitative interviews with 53 practitioners of 29 software and non-software project teams from Robert Bosch GmbH. Results: We discovered that Scrum Masters initially plays nine leadership roles which they transfer to the team while it matures. Roles can be transferred by providing a leadership gap, which allows team members to take on a leadership role, and by providing an internal team environment with communication on equal terms, psychological safety, transparency, shared understanding, shared purpose and self-efficacy. Conclusion: The Scrum Master role changes while the team matures. Trust and freedom to take over a leadership role in teams are essential enablers. Our results support practitioners in implementing agile teams in established companies.
Monolayer molybdenum disulphide (MoS$_2$) is a promising two-dimensional (2D) material for nanoelectronic and optoelectronic applications. The large-area growth of MoS$_2$ has been demonstrated using chemical vapor deposition (CVD) in a wide range of
deposition temperatures from 600 {deg}C to 1000 {deg}C. However, a direct comparison of growth parameters and resulting material properties has not been made so far. Here, we present a systematic experimental and theoretical investigation of optical properties of monolayer MoS$_2$ grown at different temperatures. Micro-Raman and photoluminescence (PL) studies reveal observable inhomogeneities in optical properties of the as-grown single crystalline grains of MoS$_2$. Close examination of the Raman and PL features clearly indicate that growth-induced strain is the main source of distinct optical properties. We carry out density functional theory calculations to describe the interaction of growing MoS$_2$ layers with the growth substrate as the origin of strain. Our work explains the variation of band gap energies of CVD-grown monolayer MoS$_2$, extracted using PL spectroscopy, as a function of deposition temperature. The methodology has general applicability to model and predict the influence of growth conditions on strain in 2D materials.
