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Herein, an artificial neural network (ANN)-based approach for the efficient automated modeling and simulation of isotropic hyperelastic solids is presented. Starting from a large data set comprising deformations and corresponding stresses, a simple, physically based reduction of the problem’s dimensionality is performed in a data processing step. More specifically, three deformation type invariants serve as the input instead of the deformation tensor itself. In the same way, three corresponding stress coefficients replace the stress tensor in the output layer. These initially unknown values are calculated from a linear least square optimization problem for each data tuple. Using the reduced data set, an ANN-based constitutive model is trained by using standard machine learning methods. Furthermore, in order to ensure thermodynamic consistency, the previously trained network is modified by constructing a pseudo-potential within an integration step and a subsequent derivation which leads to a further ANN-based model. In the second part of this work, the proposed method is exemplarily used for the description of a highly nonlinear Ogden type material. Thereby, the necessary data set is collected from virtual experiments of discs with holes in pure plane stress modes, where influences of different loading types and specimen geometries on the resulting data sets are investigated. Afterwards, the collected data are used for the ANN training within the reduced data space, whereby an excellent approximation quality could be achieved with only one hidden layer comprising a low number of neurons. Finally, the application of the trained constitutive ANN for the simulation of two three-dimensional samples is shown. Thereby, a rather high accuracy could be achieved, although the occurring stresses are fully three-dimensional whereas the training data are taken from pure two-dimensional plane stress states.

Open source software projects often rely on package management systems that help projects discover, incorporate, and maintain dependencies on other packages, maintained by other people. Such systems save a great deal of effort over ad hoc ways of advertising, packaging, and transmitting useful libraries, but coordination among project teams is still needed when one package makes a breaking change affecting other packages. Ecosystems differ in their approaches to breaking changes, and there is no general theory to explain the relationships between features, behavioral norms, ecosystem outcomes, and motivating values. We address this through two empirical studies. In an interview case study, we contrast Eclipse, NPM, and CRAN, demonstrating that these different norms for coordination of breaking changes shift the costs of using and maintaining the software among stakeholders, appropriate to each ecosystem’s mission. In a second study, we combine a survey, repository mining, and document analysis to broaden and systematize these observations across 18 ecosystems. We find that all ecosystems share values such as stability and compatibility, but differ in other values. Ecosystems’ practices often support their espoused values, but in surprisingly diverse ways. The data provides counterevidence against easy generalizations about why ecosystem communities do what they do.

In highly configurable systems, features may interact unexpectedly and produce faulty behavior. Those faults are not easily identified from the analysis of each feature separately, especially when feature specifications are missing. We propose VarXplorer, a dynamic and iterative approach to detect suspicious interactions. It provides information on how features impact the control and data flow of the program. VarXplorer supports developers with a graph that visualizes this information, mainly showing suppress and require relations between features. To evaluate whether VarXplorer helps improve the performance of identifying suspicious interactions, we perform a controlled study with 24 subjects. We find that with our proposed feature-interaction graphs, participants are able to identify suspicious interactions more than 3 times faster compared to the state-of-the-art tool.

Program comprehension is an important, but hard to measure cognitive process. This makes it difficult to provide suitable programming languages, tools, or coding conventions to support developers in their everyday work. Here, we explore whether functional magnetic resonance imaging (fMRI) is feasible for soundly measuring program comprehension. To this end, we observed 17 participants inside an fMRI scanner while they were comprehending source code. The results show a clear, distinct activation of five brain regions, which are related to working memory, attention, and language processing, which all fit well to our understanding of program comprehension. Furthermore, we found reduced activity in the default mode network, indicating the cognitive effort necessary for program comprehension. We also observed that familiarity with Java as underlying programming language reduced cognitive effort during program comprehension. To gain confidence in the results and the method, we replicated the study with 11 new participants and largely confirmed our findings. Our results encourage us and, hopefully, others to use fMRI to observe programmers and, in the long run, answer questions, such as: How should we train programmers? Can we train someone to become an excellent programmer? How effective are new languages and tools for program comprehension?

In recent years, microfluidic technologies were introduced for massively parallel cultivation and screening approaches. Individual cells can easily be singularized, compartmentalized, and cultivated from mixed inocula using droplet microfluidics. The generation of millions of droplets in a high-throughput manner enables studying diverse samples and combining the evaluation of genetic and phenotypic variants. It is a powerful tool to explore and exploit natural metabolic diversity.

Automatically repairing a buggy program is essentially a search problem, searching for code transformations that pass a set of tests. Various search strategies have been explored, but they either navigate the search space in an ad hoc way using heuristics, or systemically but at the cost of limited edit expressiveness in the kinds of supported program edits. In this work, we explore the possibility of systematically navigating the search space without sacrificing edit expressiveness. The key enabler of this exploration is variational execution, a dynamic analysis technique that has been shown to be effective at exploring many similar executions in large search spaces. We evaluate our approach on IntroClassJava and Defects4J, showing that a systematic search is effective at leveraging and combining fixing ingredients to find patches, including many high-quality patches and multi-edit patches.

In collaborative software development, it is considered to be a best practice to submit code changes as a sequence of cohesive commits, each of which records the work result of a specific development activity, such as adding a new feature, bug fixing, and refactoring. However, rather than following this best practice, developers often submit a set of loosely-related changes serving for different development activities as a composite commit, due to the tedious manual work and lack of effective tool support to decompose such a tangled changeset. Composite commits often obfuscate the change history of software artifacts and bring challenges to efficient collaboration among developers. To encourage activity-oriented commits, we propose SmartCommit, a graph-partitioning-based interactive approach to tangled changeset decomposition that leverages not only the efficiency of algorithms but also the knowledge of developers. To evaluate the effectiveness of our approach, we (1) deployed SmartCommit in an international IT company, and analyzed usage data collected from a field study with 83 engineers over 9 months; and (2) conducted a controlled experiment on 3,000 synthetic composite commits from 10 diverse open-source projects. Results show that SmartCommit achieves a median accuracy between 71–84% when decomposing composite commits without developer involvement, and significantly helps developers follow the best practice of submitting activity-oriented commits with acceptable interaction effort and time cost in real collaborative software development.

For the automated optical inspection of manufactured components with complex geometries or highly reflective surfaces, a suitable selection of measurement poses and the associated planning of the measurement trajectory is crucial. This is especially important for active triangulation measurement methods like fringe projection. Due to complex measurement object geometries or poor alignment of the measuring system the influence of multiple reflections can potentially lead to incorrect or incomplete 3-D reconstruction of the specimen surface. This paper introduces a simulative GPU-based inverse ray tracing approach to identify low-reflection measurement poses for active optical measurement systems. Starting from the virtual camera origin, rays are emitted from each camera pixel and the reflection at the measurement objects surface is calculated using the Torrence- Sparrow BRDF. With an additional approach based on Whitted raytracing, the influence of multiple reflections and the reflection depth on the rendered camera image is taken into account. By calculating the summed reflection depth of each rendered measurement sequence, a height map of the reflection frequency distribution is created. By sampling a predefined surface point on the path of a limited sphere, the comparability of possible measurement poses is achieved. Thus, local minima can be identified and the poses with the lowest reflection influence can be selected to perform a suitable trajectory planning. This a priori knowledge can also be transferred into application and used for the estimation of image areas, which captured multiple reflections. Thus for these areas specific masks are generated and can be applied in real measurements to reconstruct multiple reflection free surfaces.

Triangulation based optical measuring systems are an important element of industrial quality assurance. Due to their robustness and cost-effectiveness Laser Light Section Sensors have become a widespread solution for Geometry measurements. In order to reconstruct the scene, it is necessary to identify the corresponding laser line, which is distorted due to the geometrical properties of the specimen, in the camera image. In Order to achieve the highest precision possible, the line segmentation has to be performed at sub-pixel accuracy. Furthermore, in an industrial environment, interfering light sources may be present. A distinction between these influences and the laser light ensures a robust measurement. The projected Laser Line of a triangulation sensor is usually formed by a Powell lens from a point source, which results in a uniformly distributed intensity. Another option to achieve highly uniform intensity distributions is by means of a lenticular lens. A side effect of these optics is that the fine-structure of the projected line is formed by a chain of equidistant dots. Across the laser line the intensity distribution can be considered as a Gaussian profile. Challenges to the segmentation are from the fine, dotted structure of the line. Although conventional methods, such as centroid based algorithms can be applied, with the drawback of imprecise peak detection. In order to insure both segmentation accuracy und robustness, this paper introduces a novel segmentation method based on wavelet-transformation. In a first step the periodic fine-structure of the line is utilized for a definite identification of the line with distinction from scattered light. In a second step a gaussian wavelet is used to achieve sub-pixel accuracy in peak detection. The developed method is compared to conventional peak detection methods.

The 3D measurement of the interior of hollow technical bodies is still a challenge for state-of-the-art measurement systems. This is especially true for complex industrial free-form objects inside confined spaces. Fringe projection enables accurate and fast 3D measurements of object surfaces. To fulfill the task of geometric inspection of confined spaces, we have developed an endoscopic and flexible fringe projection system. The fringe patterns are generated with a RGB LED projector. Fibre-coupling of the structured light is achieved by using a microscope lens and bundle of coherent image fibres. A compact sensor head can be achieved by using a micro-objective for projecting the fringes and a Chip-on-Tip camera to capture the images. A micro lens with a diameter of 1.7 mm was used as the projector lens and a 2 megapixel 1/6” chip was used as the image sensor. By synchronizing the projector with the camera, the system is capable of capturing up to 10 grayscale patterns per second. The measurement volumes result to approximately 20 x 13 x 4 mm3. Typically, the measurement time is in the range of 1 - 3 s, depending on the number of projected images. Measurements on a 50 µm step standard confirmed that a measurement uncertainty of less than 29.4 ± 3.2 µm is achieved with this system. Mounted on a carrier system, the presented fringe projection system offers the possibility to enter confined areas and perform high-precision 3D measurements.

Optical triangulation systems based on fringe projection profilometry have emerged in recent years as a complement to traditional tactile devices. Due to the good scalability of the measurement approach, a highly compact novel sensor for maintenance and inspection in narrow spaces is realized by applying optical fiber bundles. Especially in the field of high-resolution and rapid maintenance in industrial environments, numerous applications arise. Endoscopic 3D measurements of gearing geometries are of particular technical relevance for detecting and quantifying damage. The measurement performance depends to a considerable extent on the technical surface to be inspected. Polished surfaces are particularly problematic due to specular reflections, but can still be partially reconstructed by using HDR imaging. However, if multiple reflections occur due to the specimen geometry and sensor arrangement in such a way that the optical path of each corresponding camera pixel can no longer be reconstructed unambiguously, a measurement is no longer feasible. In this study, the effects of surface roughness, sensor arrangement, and triangulation angle on measurement error are systematically investigated to describe possible application limits and provide guidance on sensor operation.

Detecting feature interactions is imperative for accurately predicting performance of highly-configurable systems. State-of-the-art performance prediction techniques rely on supervised machine learning for detecting feature interactions, which, in turn, relies on time-consuming performance measurements to obtain training data. By providing information about potentially interacting features, we can reduce the number of required performance measurements and make the overall performance prediction process more time efficient. We expect that information about potentially interacting features can be obtained by analyzing the source code of a highly-configurable system, which is computationally cheaper than performing multiple performance measurements. To this end, we conducted an in-depth qualitative case study on two real-world systems (mbedTLS and SQLite), in which we explored the relation between internal (precisely control-flow) feature interactions, detected through static program analysis, and external (precisely performance) feature interactions, detected by performance-prediction techniques using performance measurements. We found that a relation exists that can potentially be exploited to predict performance interactions.

Cognitive judgement bias tests have become important new tools for the assessment of animal emotions. They allow for the inference of an animal's emotional state based on ambiguous cue interpretations. As mice are the predominantly used animal model for cognitive and behavioural neuroscience, research in this field would considerably benefit from the development of suitable judgement bias tests for this species. Against this background, we aimed to implement two different active choice cognitive judgement bias paradigms for mice in a methodological study. For this purpose, two experiments were conducted: in experiment I, an automated, vision-based touchscreen technique was applied, allowing for the direct translation of tasks from rodents to humans and vice versa. Experiment II comprised a task relying on more ecologically relevant cues in form of tunnels of different lengths. While the touchscreen task was characterized by automation-related advantages such as the possibility to present many trials per session and a high convenience for the experimenter, the tunnel task was learned faster by the mice. In both tests, however, the response to the trained and ambiguous conditions resulted in a graded curve, the basic requirement for proving task validity. Thus, both the translational touchscreen task as well as the ecologically more relevant tunnel task could successfully be implemented and provide new tools for the future assessment of cognitive judgement biases in mice.

The large amount of third-party packages available in fast-moving software ecosystems, such as Node.js/npm, enables attackers to compromise applications by pushing malicious updates to their package dependencies. Studying the npm repository, we observed that many packages in the npm repository that are used in Node.js applications perform only simple computations and do not need access to filesystem or network APIs. This offers the opportunity to enforce least-privilege design per package, protecting applications and package dependencies from malicious updates. We propose a lightweight permission system that protects Node.js applications by enforcing package permissions at runtime. We discuss the design space of solutions and show that our system makes a large number of packages much harder to be exploited, almost for free., 13 pages

Based the fringe projection profilometry, a compact and flexible positionable measuring head can be combined with optical fiber bundles to perform in-situ inspection tasks in industrial applications. Surfaces of complex geometries can be reconstructed and quantified in metric coordinates by means of a fast, non-contact and high-resolution measurement. Defect segmentation, on the other hand, is rather complex with three-dimensional point clouds, since reference data is required or a deviation determination is ambiguous and susceptible to errors. Due to each reconstructed object point corresponding to a camera pixel, it is possible to apply image processing algorithms or neural networks for defect segmentation. Since image based segmentation is more susceptible to poor illumination and deviating surface curvature or texture, a circular array of miniature LEDs has been coaxially arranged around the imaging optics of the camera’s fiber to provide different illumination directions. By utilizing a directional variable illumination sequence, the advantages of image-based segmentation can be combined with the unambiguousness and metric quantifiability of point cloud data.

The rapid developments in the micro camera industry, driven by the smartphone sector, offer a wide range of innovations with respect to the performance and miniaturization of endoscopic instruments. For the fast 3D inspection of inaccessible components such as turbine blades in partially disassembled aircraft engines, a borescopic fringe projection system was developed. This study provides a methodology for the comparison of different camera and projection configurations within borescopic fringe projection. Furthermore, the current limits for the use of multimedia sensors in metrological applications are shown and quantified. The projection unit of the developed measuring system is based on a digital micromirror device, which generates structured light that is imaged into the measurement scene by means of an objective lens and a borescope. Threedimensional, high-resolution reconstructions are carried out via chip-on-the-tip miniature cameras based on the MIPI interface by forming a triangulation base with the projection unit. To enable inspections in confined spaces, the cameras are connected to external framegramer boards. In this study, 1/6” and 1/4” sensors with fixedfocus lenses are evaluated to assess the trade-off between the physical sensor size and the possible reconstruction accuracy. Typical camera parameters such as the sensitivity with respect to the signal-to-noise ratio are determined by means of a standardized test setup. In addition, the application in the triangulation system is evaluated through the modulation signal strength as well as the suitability for typical system calibrations based on the widely used pinhole camera model in combination with a distortion polynomial correction according to the approach of Conrady and Brown. Finally, the sensors are compared regarding 3D reconstructions of calibrated geometric features in accordance with ISO/IEC Guide 98-3:2008 and VDI/VDE 2634-2.

Fringe projection profilometry in combination with other optical measuring technologies has established itself over the last decades as an essential complement to conventional, tactile measuring devices. The non-contact, holistic reconstruction of complex geometries within fractions of a second in conjunction with the lightweight and transportable sensor design open up many fields of application in production metrology. Furthermore, triangulation-based measuring principles feature good scalability, which has led to 3D scanners for various scale ranges. Innovative and modern production processes, such as sheet-bulk metal forming, thus, utilize fringe projection profilometry in many respects to monitor the process, quantify possible wear and improve production technology. Therefore, it is essential to identify the appropriate 3D scanner for each application and to properly evaluate the acquired data. Through precise knowledge of the measurement volume and the relative uncertainty with respect to the specimen and scanner position, adapted measurement strategies and integrated production concepts can be realized. Although there are extensive industrial standards and guidelines for the quantification of sensor performance, evaluation and tolerancing is mainly global and can, therefore, neither provide assistance in the correct, application-specific positioning and alignment of the sensor nor reflect the local characteristics within the measuring volume. Therefore, this article compares fringe projection systems across various scale ranges by positioning and scanning a calibrated sphere in a high resolution grid.

Optical 3D geometry reconstruction, or more specific, fringe projection profilometry, is a state-of-the-art technique for the measurement of the shape of objects in confined spaces or under rough environmental conditions, e.g., while inspecting a wrought-hot specimen after a forging operation. While the contact-less method enables the measurement of such an object, the results are influenced by the light deflection effect occurring due to the inhomogeneous refractive index field induced by the hot air around the measurand. However, the developed active compensation methods to fight this issue exhibits a major drawback, namely an additional cooling of the object and a subsequent transient illumination component. In this paper, we investigate the cooling and its effect on temporal phase reconstruction algorithms and take a theoretical approach to its compensation. The simulated compensation measures are transferred to a fringe projection profilometry setup and are evaluated using established and newly developed methods. The results show a significant improvement when measuring specimens under a transient illumination and are easily transferable to any kind of multi-frequency phase-shift measurement. Dataset License: CC-BY

Integration is a grand challenge for many contemporary research endeavors. Mechanistic explanations provide a multi-level approach especially suited to bring out different aspects of the causal-mechanical structure of the world. Yet, we encounter a triad of differently structured producing, underlying and maintaining mechanistic explanations. Understanding how the elements of this triad can be fruitfully and systematically linked, I suggest, may help drive scientific progress and integration. This paper discusses important conceptual ties between an explanandum and the metaphysical relations highlighted in the corresponding explanans: to explain how an end product or result is generated, scientists will usually search for the mechanism that produced it; to explain a process, they will typically search for the mechanism underlying it; and to explain how a system’s stable state or continuous behavior is maintained, they search for the mechanism maintaining it. Appreciating these different projects, and understanding the connections between them, provides an important backdrop for explanatory integration. Besides, it allows us to reconcile apparently different conceptions of mechanistic explanations without heavy metaphysical baggage.

As a result of an increasingly automatized and digitized industry, processes are becoming more complex. Augmented Reality has shown considerable potential in assisting workers with complex tasks by enhancing user understanding and experience with spatial information. However, the acceptance and integration of AR into industrial processes is still limited due to the lack of established methods and tedious integration efforts. Meanwhile, deep neural networks have achieved remarkable results in computer vision tasks and bear great prospects to enrich Augmented Reality applications . In this paper, we propose an Augmented-Reality-based human assistance system to assist workers in complex manual tasks where we incorporate deep neural networks for computer vision tasks. More specifically, we combine Augmented Reality with object and action detectors to make workflows more intuitive and flexible. To evaluate our system in terms of user acceptance and efficiency, we conducted several user studies. We found a significant reduction in time to task completion in untrained workers and a decrease in error rate. Furthermore, we investigated the users learning curve with our assistance system., 6 pages, 7 figures

Mobile robots have gained increased importance within industrial tasks such as commissioning, delivery or operation in hazardous environments. The ability to navigate in unknown and complex environments is paramount in industrial robotics. Reinforcement learning approaches have shown remarkable success in dealing with unknown situations and react accordingly without manually engineered guidelines and overconservative measures. However, these approaches are often restricted to short range navigation and are prone to local minima due to a lack of a memory module. Thus, the navigation in complex environments such as mazes, long corridors or concave areas is still an open frontier. In this paper, we incorporate a variety of recurrent neural networks to cope with these challenges. We train a reinforcement learning based agent within a 2D simulation environment of our previous work and extend it with a memory module. The agent is able to navigate solely on sensor data observations which are directly mapped to actions. We evaluate the performance on different complex environments and achieve enhanced results within complex environments compared to memory-free baseline approaches.

Performance-influence models can help stakeholders understand how and where configuration options and their interactions influence the performance of a system. With this understanding, stakeholders can debug performance behavior and make deliberate configuration decisions. Current black-box techniques to build such models combine various sampling and learning strategies, resulting in tradeoffs between measurement effort, accuracy, and interpretability. We present Comprex, a white-box approach to build performance-influence models for configurable systems, combining insights of local measurements, dynamic taint analysis to track options in the implementation, compositionality, and compression of the configuration space, without relying on machine learning to extrapolate incomplete samples. Our evaluation on 4 widely-used, open-source projects demonstrates that Comprex builds similarly accurate performance-influence models to the most accurate and expensive black-box approach, but at a reduced cost and with additional benefits from interpretable and local models., Comment: Accepted for publication at ICSE'21

Automation tools like continuous integration services, code coverage reporters, style checkers, dependency managers, etc. are all known to provide significant improvements in developer productivity and software quality. Some of these tools are widespread, others are not. How do these automation "best practices" spread? And how might we facilitate the diffusion process for those that have seen slower adoption? In this paper, we rely on a recent innovation in transparency on code hosting platforms like GitHub---the use of repository badges---to track how automation tools spread in open-source ecosystems through different social and technical mechanisms over time. Using a large longitudinal data set, multivariate network science techniques, and survival analysis, we study which socio-technical factors can best explain the observed diffusion process of a number of popular automation tools. Our results show that factors such as social exposure, competition, and observability affect the adoption of tools significantly, and they provide a roadmap for software engineers and researchers seeking to propagate best practices and tools.

Sheet-bulk metal forming, as a novel and innovative production process, represents a possible approach for the realization of present and future requirements in the context of production technology through the combination of different, forming processes. Due to the metrological requirements of modern, digital and largely automated production plants, the development of new types of measuring instruments for use in industrial environments is driven. Within the scope of the TCRC 73, a new type of compact and flexible endoscopic 3D sensor was developed in subproject B6, enabling measurements of stressed structures inside a sheet-bulk metal forming plant. This serves the purpose of preventing failure and obtaining a better understanding of the process. This contribution provides a description of the optical design, the principal components, sensor head configurations, as well as a description of algorithms and models and demonstrates them by reference to applications.

Developers often use the C preprocessor to handle variability and portability. However, many researchers and practitioners criticize the use of preprocessor directives because of their negative effect on code understanding, maintainability, and error proneness. This negative effect may lead to configuration-related code weaknesses, which appear only when we enable or disable certain configuration options. A weakness is a type of mistake in software that, in proper conditions, could contribute to the introduction of vulnerabilities within that software. Configuration-related code weaknesses may be harder to detect and fix than weaknesses that appear in all configurations, because variability increases complexity. To address this problem, we propose a sampling-based white-box technique to detect configuration-related weaknesses in configurable systems. To evaluate our technique, we performed an empirical study with 24 popular highly configurable systems that make heavy use of the C preprocessor, such as Apache Httpd and Libssh. Using our technique, we detected 57 configuration-related weaknesses in 16 systems. In total, we found occurrences of the following five kinds of weaknesses: 30 memory leaks, 10 uninitialized variables, 9 null pointer dereferences, 6 resource leaks, and 2 buffer overflows. The corpus of these weaknesses is a valuable source to better support further research on configuration-related code weaknesses.

For configurable systems, features developed and tested separately may present a different behavior when combined in a system. Since software products might be composed of thousands of features, developers should guarantee that all valid combinations work properly. However, features can interact in undesired ways, resulting in failures. A feature interaction is an unpredictable behavior that cannot be easily deduced from the individual features involved. We proposed VarXplorer to inspect feature interactions as they are detected and incrementally classify them as benign or problematic. Our approach provides an iterative analysis of feature interactions allowing developers to focus on suspicious cases. In this paper, we present an experimental study to evaluate our iterative process of tests execution. We aim to understand how VarXplorer could be used for a faster and more objective feature interaction analysis. Our results show that VarXplorer may reduce up to 50% the amount of interactions a developer needs to check during the testing process.

Highly-configurable systems provide significant reuse opportunities by tailoring system variants based on a set of features. Those features can interact in undesired ways which may result in faults. Thus, we propose VarXplorer, a dynamic and iterative approach to detect suspicious interactions. To evaluate whether VarXplorer helps improving the performance of identifying suspicious interactions, we performed two empirical studies. Our results shows that from the VarXplorer graphs, participants are able to identify suspicious interactions more than 3 times faster compared to the state-of-the-art tool. Additionally, the iterative detection process provides a more efficient feature interaction analysis, reducing the data developers needs to check to find problematic interactions.

We have developed and implemented the only widely used sound WCET analysis technology. The claim of soundness is a strong one, given the many necessary component techniques of WCET analysis and the need to incorporate an abstraction of the execution platform. This article deals with the qualification of the AbsInt timing-analysis tool, aiT, as required by several international standards for safety-critical software. We briefly sketch these requirements, give a short description of our timing-analysis method, and then concentrate on the most complex part, the microarchitectural analysis. This analysis phase uses an abstraction of the execution platform. To arrive at this abstraction is a complex, error-prone process. The main focus of the article is the technique to validate the abstract execution platform by trace validation.

Machine learning techniques allow a direct mapping of atomic positions and nuclear charges to the potential energy surface with almost ab initio accuracy and the computational efficiency of empirical potentials. In this work, we propose a machine learning method for constructing high-dimensional potential energy surfaces based on feed-forward neural networks. As input to the neural network, we propose an extendable invariant local molecular descriptor constructed from geometric moments. Their formulation via pairwise distance vectors and tensor contractions allows a very efficient implementation on graphical processing units (GPUs). The atomic species is encoded in the molecular descriptor, which allows the restriction to one neural network for the training of all atomic species in the data set. We demonstrate that the accuracy of the developed approach in representing both chemical and configurational spaces is comparable to the one of several established machine learning models. Due to its high accuracy and efficiency, the proposed machine-learned potentials can be used for any further tasks, for example, the optimization of molecular geometries, the calculation of rate constants, or molecular dynamics.

We show how Gaussian process regression can be used to update Hessian matrices using gradient-based information in the course of an optimization procedure. This is done by building a Gaussian process with at least one initial Hessian and some further energies and gradients from electronic structure calculations and evaluating the desired second derivative of the resulting Gaussian process. To a certain extent, we can overcome the significant scaling problems that occur when training a Gaussian process with Hessian information. We demonstrate in benchmark runs using the partitioned rational function optimization (P-RFO) that this new update method can outperform classical Hessian update methods for small systems.

Feature flags (a.k.a feature toggles) are a mechanism to keep new features hidden behind a boolean option during development. Flags are used for many purposes, such as A/B testing and turning off a feature more easily in case of failures. While software engineering research on feature flags is burgeoning, examples of software projects using flags rarely come from outside commercial and private projects, stifling academic progress. To address this gap, in this paper we present a novel semi-automated mining software repositories approach to detect feature flags in open-source projects, based on analyzing the projects' commit messages and other project characteristics. With our approach, we search over all open-source GitHub projects, finding multiple thousand plausible and active candidate feature flagging projects. We manually validate projects and assemble a dataset of 100 confirmed feature flagging projects. To demonstrate the benefits of our detection technique, we report on an initial analysis of feature flags in the validated sample of 100 projects, investigating practices that correlate with shorter flag lifespans (typically desirable to reduce technical debt), such as using the issue tracker and having a flag owner.

The notion of forking has changed with the rise of distributed version control systems and social coding environments, like GitHub. Traditionally forking refers to splitting off an independent development branch (which we call hard forks); research on hard forks, conducted mostly in pre-GitHub days showed that hard forks were often seen critical as they may fragment a community. Today, in social coding environments, open-source developers are encouraged to fork a project in order to contribute to the community (which we call social forks), which may have also influenced perceptions and practices around hard forks. To revisit hard forks, we identify, study, and classify 15,306 hard forks on GitHub and interview 18 owners of hard forks or forked repositories. We find that, among others, hard forks often evolve out of social forks rather than being planned deliberately and that perception about hard forks have indeed changed dramatically, seeing them often as a positive non-competitive alternative to the original project.

Feature flags for continuous deployment and configuration options for customizing software share many similarities, both conceptually and technically. However, neither academic nor practitioner publications seem to clearly compare these two concepts. We argue that a distinction is valuable, as applications, goals, and challenges differ fundamentally between feature flags and configuration options. In this work, we explore the differences and commonalities of both concepts to help understand practices and challenges, and to help transfer existing solutions (e.g., for testing). To better understand feature flags and how they relate to configuration options, we performed nine semi-structured interviews with feature-flag experts. We discovered several distinguishing characteristics but also opportunities for knowledge and technology transfer across both communities. Overall, we think that both communities can learn from each other.

Developers from open-source communities have reported high stress levels from frequent demands for features and bug fixes and from the sometimes aggressive tone of these demands. Toxic conversations may demotivate and burn out developers, creating challenges for sustaining open source. We outline a path toward finding, understanding, and possibly mitigating such unhealthy interactions. We take a first step toward finding them, by developing and demonstrating a measurement instrument (an SVM classifier tailored for software engineering) to detect toxic discussions in GitHub issues. We used our classifier to analyze trends over time and in different GitHub communities, finding that toxicity varies by community and that toxicity decreased between 2012 and 2018.

In globally distributed software development, many software developers have to collaborate and deal with issues of collaboration. Although collaboration is challenging, collaborative development produces better software than any developer could produce alone. Unlike previous work which focuses on the proposal and evaluation of models and tools to support collaborative work, this paper presents an interview study aiming to understand (i) the motivations, (ii) how collaboration happens, and (iii) the challenges and barriers of collaborative software development. After interviewing twelve experienced software developers from GitHub, we found different types of collaborative contributions, such as in the management of requests for changes. Our analysis also indicates that the main barriers for collaboration are related to non-technical, rather than technical issues.

Augmented Reality has been subject to various integration efforts within industries due to its ability to enhance human machine interaction and understanding. Neural networks have achieved remarkable results in areas of computer vision, which bear great potential to assist and facilitate an enhanced Augmented Reality experience. However, most neural networks are computationally intensive and demand huge processing power thus, are not suitable for deployment on Augmented Reality devices. In this work we propose a method to deploy state of the art neural networks for real time 3D object localization on augmented reality devices. As a result, we provide a more automated method of calibrating the AR devices with mobile robotic systems. To accelerate the calibration process and enhance user experience, we focus on fast 2D detection approaches which are extracting the 3D pose of the object fast and accurately by using only 2D input. The results are implemented into an Augmented Reality application for intuitive robot control and sensor data visualization. For the 6D annotation of 2D images, we developed an annotation tool, which is, to our knowledge, the first open source tool to be available. We achieve feasible results which are generally applicable to any AR device thus making this work promising for further research in combining high demanding neural networks with Internet of Things devices.

Higher-order mutation has the potential for improving major drawbacks of traditional first-order mutation, such as by simulating more realistic faults or improving test optimization techniques. Despite interest in studying promising higher-order mutants, such mutants are difficult to find due to the exponential search space of mutation combinations. State-of-the-art approaches rely on genetic search, which is often incomplete and expensive due to its stochastic nature. First, we propose a novel way of finding a complete set of higher-order mutants by using variational execution, a technique that can, in many cases, explore large search spaces completely and often efficiently. Second, we use the identified complete set of higher-order mutants to study their characteristics. Finally, we use the identified characteristics to design and evaluate a new search strategy, independent of variational execution, that is highly effective at finding higher-order mutants even in large code bases.

The quality of optical measurements is significantly affected by the reflection properties of the measured component. Therefore, it is important to consider the properties of the reflective surface to obtain accurate measurement results. A common method for the mathematical representation of reflections is the bidirectional reflection distribution function (BRDF). Typically BRDFs are measured via a gonioreflectometer. However, these are often only applicable on flat specimens or objects with previously known geometric properties. This paper presents an approach for the measurement of the BRDF on inhomogeneous freeform surfaces. For this purpose, a robot-assisted multisensor system is used consisting of a fringe projection sensor and an industrial camera, which is modified with six light sources that are evenly distributed around the optical axis and point at the measuring object. The reflection measurement consists of the sequential image acquisition of individual lighting configurations by successively switched on light sources. With the assumption of isotropic surface properties and known position of each individual light source, the relative BRDF can be determined pixel by pixel. This enables the BRDF measurement of freeform surfaces with varying reflection properties. Knowing the transformation between both sensor coordinate systems, the resulting BRDF data can be projected onto the points of the fringe projection measurement for geometrical representation. As an application example, a damage characterization of surfaces, based on the measured BRDF data is presented. For this purpose, a worn turbine blade of an aircraft engine is characterized so that burnt regions on the components’ surface can be detected.

When repairing worn components it is crucial to have detailed knowledge of the current object’s state. For this purpose a multi-sensor system was developed to measure objects in different scales and modalities. This work focuses on the 3-D measurement of worn turbine blades using a fringe projection system. The 3-D geometry of turbine blades is crucial for the overall performance and safety of an engine. Therefore it is not sufficient to rely on single fringe projection measurements for a functional evaluation. To obtain a 3-D model the blade has to be measured from multiple directions. Gathered data are combined to form the model. This process is called registration or stitching. To reduce uncertainties during the process markers can be applied on or near the measurement object. However, common methods using markers are insufficient in automatability and feature density and therefore are not applicable in this case. In this work a novel registration strategy based on projected random patterns is developed. Multiple projectors are placed around the object to illuminate its geometry. Keypoints are identified by capturing additional grayscale images and applying state-of-the art feature detection algorithms. Feature matching is performed on consecutive measurements. Matches are preprocessed and a random sample consensus approach is chosen to calculate the rigid body transformation. Multiple measurements of the turbine blade and other geometries have been successfully aligned using the proposed strategy. Beyond that the high density of features allows the alignment of measurements with different scales and resolutions.

The realization of 3D triangulation measurements in inhomogeneous media is challenging, as the sensor light path is not necessarily rectilinear anymore and the triangulation principle is violated. An exemplary measurement scenario under inhomogeneous optical conditions is the geometry characterization through an inspection window. The discrete refractive index variation from air to inspection window and back to air (or even water) can require complicated light path modeling approaches in order to triangulate 3D surface data correctly. As commonly used entocentric lenses “fan out” the projected light rays, the rays’ incidence angles onto the refractive index interface are not constant, and the rays are individually deflected. In consequence, the typically used camera pinhole model does not apply anymore, or can only approximate the actual light path under refraction. In this paper, we present a structured light sensor concept for measurements through inspection windows, which does not require a special adaption of the light path model. The key is the application of a telecentric stereo camera pair and high-quality optical inspection windows. It can be shown theoretically, that no additional parameters are necessary to model light refraction induced by a plane-parallel plate – such as a window –, when used in combination with a telecentric lens. Next to the affine stereo camera pair, the sensor comprises an entocentric projector unit as feature generator. In previous work, the projector was calibrated with refraction model in order to provide a 3D point cloud basis for the affine camera calibration.1 In the new approach, the projector is merely used as feature generator to solve the correspondence problem between the affine stereo camera pair. Besides the developed sensor hardware concept, we present an overview on the calibration strategy based on an affine self-calibration approach, the solving of the correspondence problem between the cameras, and first calibration and measurement results. In forthcoming work, the sensor is also meant to be used in three-media refraction scenarios.

Hot forming processes, especially open die forging, are often used for production of high-performance, large-scale objects. The main benefits compared to, e.g. shape cutting methods, include lower material use and higher stress resistance. Inline process control by 3d geometry measurement is an important part of a cost-effective component production. However, there are no automated control systems commercially available for open die forging, which results in a limited precision of the final component geometry. The main challenges for a control system in said conditions are imposed by the temperature influence of the hot object on the measurement systems as well as limited actuator accuracy for the precise handling of hot, heavy objects. Additionally, the tools used in open die forging are kept simple for financial reasons. Comparable tools for, e.g., drop forging, need to be exclusively made for each new object form and therefore cannot be used for a cost-efficient production of low-quantity components. In this paper, we present a production concept in order to control a hot forming method for large scale, low quantity components. The approach combines an adaptable high-resolution 3d geometry measurement system and an incremental open die forging press for cost- and time-efficient production. Forming simulations will need to be conducted prior to the process to gain access to a large database of possible forming steps to reach the desired final geometry. The control system itself compares the measured geometry and temperature to the simulated ones. Occurring deviations are analysed and a sequence of forming steps is calculated from the database. The necessary forging forces and strokes of the actuating system are extracted from the chosen forming sequence and linked back into the system to achieve maximum precision.

The regeneration of aircraft engines provides a key approach in order to reduce operating costs of these complex capital goods. The efficient and resource-saving maintenance of aircraft engines is particularly challenging, as they are made from a variety of different parts with locally adapted properties and different functionalities, which therefore also differ in the requirements in surface quality. The engine parts are manufactured and repaired on different machines, such as milling or turning machines. The subsequent quality check is realized with the help of highly specialized metrology systems. In case of so-called aircraft blisks, tactile coordinate measuring machines are used to measure the blisks surface data in regions with limited accessibility. The state-of-the-art regeneration procedure is therefore time-consuming and costly, as the parts need to be moved and mounted several times. In this paper, we present a blisk regeneration approach, which is meant to allow the inline quality monitoring of the regeneration process within a milling machine by means of a rigid endoscopic fringe projection system. The presented approach is meant to be time- and resource-saving as the fringe projection system is directly used in the blisk repair environment. To this end, we present the developed measurement system, outline the planned system integration into the milling machine and define challenges that arise due to the calibration of optical measurement systems within the production environment. The borescope with a chip-on-the-tip camera at the measuring head offers the possibility to drive into limited space and to perform high-precision 3-D measurements.

We show that a widely used benchmark set for the comparison of static”=analysis tools exhibits an impressive number of weaknesses, and that the internationally accepted quantitative”=evaluation metrics may lead to useless results. The weaknesses in the benchmark set were identified by applying a sound static analysis to the programs in this set and carefully interpreting the results. We propose how to deal with weaknesses of the quantitative metrics and how to improve such benchmarks and the evaluation process, in particular for external evaluations, in which an ideally neutral institution does the evaluation, whose results potential clients can trust. We also show that sufficiently high quality of the test cases makes an automatic result evaluation possible.

The choice of the programming language is a fundamental decision to be made when defining a safety-oriented software development process. It has significant impact on code quality and performance, but also on the achievable level of safety, the development and verification effort, and on the cost of tool qualification. Traditionally, safety-critical systems have been programmed in C or ADA. In recent years, also C++ has entered into the discussion. C++ enables elegant programming, but its inherent language complexity is much higher compared to C. This has implications for testability, structural coverage, performance, and code analysis. Further issues to be considered are tool chain diversity, the role of the standard library, and tool qualification for compilers, analyzers and other development tools. This article summarizes the requirements of different safety norms, illustrates development and verification challenges and addresses tool qualification.