Reviewerly/exHarmony huggingface dataset:
Modelling Uncertainty for Requirements: The Case of Surprises

The concept of Surprise has been used to model uncertainty for self-adaptive systems (SAS). The uncertainty in the environment of SAS demands the system to perform self-adaptation decisions, and therefore makes it hard to formulate, validate and manage their non-functional requirements (NFRs). A number of probabilistic measures exist that compute surprise to flag up situations of uncertainty for NFRs. A problem with these measures is that they don't give any information about how big or small the surprise is, and therefore lack support for quantification of the level of uncertainty and its impact on NFRs. The challenge here is to classify the size of surprise to better model uncertainty levels for NFRs. We argue based on classification, surprise can be used to identify failure situations for NFRs, and thereby support modelling of risks. In this paper, we propose a framework to allow for the classification of surprise. Based on this framework, we perform an analysis into risk for the NFRs. As a proof of concept, we have applied the framework to case of Remote Mirroring. The risk analysis is performed on both individual NFRs as well as combinations of them to demonstrate the framework's utility across a variety of tasks.
Welcome to the Fifth International Workshop on Affective Computing for Requirements Engineering (AffectRE'23)

A welcome message from the organization chairs of the fifth edition of the International Workshop on Affective Computing for Requirements Engineering (AffectRE'23), co-located with the 31'st IEEE International Requirements Engineering Conference (RE'23) in Hannover, Germany.
Using ChatGPT to Generate Human-Value User Stories as Inspirational Triggers

Recent work has recognized the importance of developing and deploying software systems that reflect human values and has explored different approaches for eliciting these values from stakeholders. However, prior studies have also shown that it can be challenging for stakeholders to specify a diverse set of product-related human values. In this paper we therefore explore the use of ChatGPT for generating user stories that describe candidate human values. These generated stories provide inspiration to stakeholder discussions and enrich the human-created user stories. We engineer a series of ChatGPT prompts to retrieve a list of common stakeholders and candidate features for a targeted product, and then, for each pairwise combination of role and feature, and for each individual Schwartz value, we issue an additional prompt to generate a candidate user story reflecting that value. We present the candidate user-stories to stakeholders and, as part of a creative requirements engineering session, we ask them to assess and prioritize the generated user-stories, and then use them as inspiration for discussing and specifying their own product-related human values. Through conducting a series of focus groups we compare the human-values created by stakeholders with and without the benefit of the ChatGPT examples. Results are evaluated with respect to coverage of values, clarity of expression, internal completeness, and through feedback from our participants. Results from our analysis show that the ChatGPT-generated user stories are able to provide creativity triggers that help stakeholders to specify human values for a product.
User Driven Functionality Deletion for Mobile Apps

Evolving software with an increasing number of features is harder to understand and thus harder to use. Software release planning has been concerned with planning these additions. Moreover, software of increasing size takes more effort to be maintained. In the domain of mobile apps, too much functionality can easily impact usability, maintainability, and resource consumption. Hence, it is important to understand the extent to which the law of continuous growth applies to mobile apps. Previous work showed that the deletion of functionality is common and sometimes driven by user reviews. However, it is unknown whether these deletions are visible or important to the app users. In this study, we surveyed 297 mobile app users to understand the significance of functionality deletion for them. Our results showed that for most users, the deletion of features corresponds with negative sentiments and change in usage and even churn. Motivated by these preliminary results, we propose Radiation to input user reviews and recommend if any functionality should be deleted from an app's User Interface (UI). We evaluate Radiation using historical data and surveying developers' opinions. From the analysis of 190,062 reviews from 115 randomly selected apps, we show that Radiation can recommend functionality deletion with an average F-Score of 74% and if sufficiently many negative user reviews suggest so.
Based on Past Experience: Highlighting Potential Human Value Issues in Domain Modelling

In this technologically evolving era, important human values such as freedom and social responsibility are frequently overlooked in software systems, which can have significant negative social consequences as can be seen by recent examples involving Facebook or Delta Airlines. Therefore, it is important to help software developers incorporate human values considerations throughout the software development process. In this paper, we focus on domain modelling with class diagrams, an important technique for requirements engineering and early design activities. We propose a domain-specific language called HVT (Human Value Trigger) that enables the collection of human value issues including how to mitigate them. Practitioners may utilize this language to contribute more such examples to grow a catalogue of these past experiences over time. As a motivating example, we analyze the domain model of WhatsApp through the lens of Schwartz's taxonomy of human values to compile a list of issues concerning human values (i.e., model elements that may affect various human values). Furthermore as proof-of-concept, a prototype implementation addresses the need for human values to be integrated in domain models with the help of these collected past experiences by providing suggestions based on the model element type, name, and semantics based on synonyms. An analysis of eight synonym services is performed to find the optimal synonym service or combination of synonym services to use with the implementation.
On the Use of GPT-4 for Creating Goal Models: An Exploratory Study

The emergence of large language models and conversational front-ends such as ChatGPT is revolutionizing many software engineering activities. The extent to which such technologies can help with requirements engineering activities, especially the ones surrounding modeling, however, remains to be seen. This paper reports on early experimental results on the potential use of GPT-4 in the latter context, with a focus on the development of goal-oriented models. We first explore GPT-4's current knowledge and mastering of a specific modeling language, namely the Goal-oriented Requirement Language (GRL). We then use four combinations of prompts – with and without a proposed textual syntax, and with and without contextual domain knowledge – to guide the creation of GRL models for two case studies. The first case study focuses on a well-documented topic in the goal modeling community (Kids Help Phone), whereas the second one explores a context for which, to our knowledge, no public goal models currently exist (Social Housing). We explore the interactive construction of a goal model through specific follow-up prompts aimed to fix model issues and expand on the model content. Our results suggest that GPT-4 preserves considerable knowledge on goal modeling, and although many elements generated by GPT-4 are generic, reflecting what is already in the prompt, or even incorrect, there is value in getting exposed to the generated concepts, many of which being non-obvious to stakeholders outside the domain. Furthermore, aggregating results from multiple runs yields a far better outcome than from any individual run.
NL2PD: A Tool for Problem Diagram Generation from Requirements in Natural Language

Requirements include the core functionalities of the software, however, due to their typical representations in natural language, professionals invest considerable effort in modeling to achieve more precise, coherent, and verifiable requirements analysis. This paper proposes a method called NL2PD for Problem Frames approach to extract requirements entities and relationships from requirements texts described in natural language and generate a problem diagrams to assist software requirements analysis. Given a requirements document in natural language, we can automatically extract required information and generate problem diagrams, on top of which users can edit, import and export. The method was tested with common requirements formats, demonstrating its ability to streamline the process of constructing problem diagrams from requirements. A demo video of this tool is available at https://youtu.be/SxoTikU1Mek.
Enhancing Requirements Elicitation through App Stores Mining: Health Monitoring App Case Study

Traditional requirements engineering methods typically involve interviews, observations, and questionnaires administered to stakeholders. Our hypothesis posits that this integral process can be significantly accelerated, and even enhanced, through strategic mining of app stores. App stores, such as Google Play and Apple Store, host millions of applications, making it likely to identify apps with features similar to the ones we intend to develop. These apps provide a wealth of information, including app descriptions, app reviews, and user interfaces. However, due to the vast quantity of apps, manual analysis of this information can be labor-intensive. To bridge this gap, our research aims to develop an approach called ReqRec for mining app stores, which will reduce the effort required by requirements engineers during the phase of requirements elicitation. To demonstrate the effectiveness of ReqRec, it will be applied to a case study involving the designing of a health monitoring application.
Improving the Efficiency of Cryptography Algorithms on Resource-Constrained Embedded Systems via RISC-V Instruction Set Extensions

This work presents the design and evaluation of RISC-V implementations of AES-128, AES-256, SHA-256, and SHA-512, both with and without specialized instructions from the Zkne and Zknh ISA extensions. In our evaluation, we use the Ibex implementation of the RISC-V ISA, a simple low-area 2-stage pipeline design, and the TinyCrypt library, a collection of low-overhead C implementations of widely employed cryptography algorithms. Several criteria relevant to low-complexity embedded systems are measured and compared, such as area costs for the hardware side; stack usage and code density for the software side; illustrating the trade-offs emerging from using specialized RISC-V instructions in the aforementioned algorithms. Clock cycle count gains of 42.57x, 44.81x, 1.45x and 1.74x were observed, as well as 4.16x, 4.16x, 1.58x and 1.63x gains in memory usage efficiency and 27.81x, 28.91x, 1.45x and 1.79x gains in energy efficiency, with an overhead of 10% in die area cost. The extended TinyCrypt library with hardware accelerated implementations and extended Ibex processor RTL are available open-source at https://github.com/cggewehr/RISCV-crypto.
Trustworthy Dynamic Object Tracking Using Deep Reinforcement Learning with the Self-Attention Mechanism

Object tracking serves as a prerequisite and foundation for higher-level driving tasks and has broad application prospects in various fields, including intelligent logistics and autonomous driving. In this paper, we propose a deep reinforcement learning-based object tracking control network model, that incorporates attention and Long Short-Term Memory (LSTM) mechanisms. The Asynchronous Advantage Actor-Critic (A3C) algorithm is employed for multi-threaded synchronous unsupervised training of the tracking network model, resulting in end-to-end dynamic object tracking control. Additionally, the Gradient-weighted Class Activation Mapping (Grad-CAM) method is utilized to analyze the interpretability of the network model. Experimental results demonstrate that by introducing the attention salience mechanism and LSTM temporal mechanism, the network can effectively focus on obstacle and target locations, thus enhancing its attentional capacity and interpretability. The trustworthiness of the object tracking model can be improved in terms of both tracking performance and interpretability.
3D Scene Graph Prediction on Point Clouds Using Knowledge Graphs

3D scene graph prediction is a task that aims to concurrently predict object classes and their relationships within a 3D environment. As these environments are primarily designed by and for humans, incorporating commonsense knowledge regarding objects and their relationships can signifi-cantly constrain and enhance the prediction of the scene graph. In this paper, we investigate the application of commonsense knowledge graphs for 3D scene graph prediction on point clouds of indoor scenes. Through experiments conducted on a real-world indoor dataset, we demonstrate that integrating external commonsense knowledge via the message passing method leads to a 15.0% improvement in scene graph prediction accuracy with external knowledge and 7.96% with internal knowledge when compared to state-of-the-art algorithms. We also tested in the real world with 10 frames per second for scene graph generation to show the usage of the model in a more realistic robotics setting.
Optimization Framework for Global Path Planning and Local Motion Planning for Robotic Welding of Multiple Large Industrial Parts

To automate the welding of multiple industrial parts that are too large for conveyor belts, we designed a mobile manipulator to perform welding around large parts and move between them. In our previous paper, we devised a motion planner to optimize the mobile welding motion. In this paper, we complete the automation by proposing a framework to optimize the path traveled between these large parts and the welding paths around each part. We model it as a Traveling Salesman Problem with Neighborhoods (TSPN), where the neighborhoods represent the parts, and the points in the neighborhoods represent the robot's welding positions around the parts. As opposed to previous TSPN solvers, our method optimizes the total path, which is the sum of both paths between neighborhoods (global path) and inside each neighborhood (local path). Also, entry and exit points for each neighborhood are defined to remove unnecessary traveling back to the entry point. In a simulation test case, our method resulted in path length reduction of 13.2% compared to the method with same entry and exit and without considering the welding paths. The scalability of this method was tested by adding more parts, which resulted in a higher path length reduction of 17%. Complexity was also increased by adding obstacles, which resulted in an even higher path length reduction of 25.5%. In our ship hull parts welding simulation, we show that our method is able to calculate an optimized path that visits all parts and the welding points around the parts. It shows that with our approach, the automation of large part welding is feasible as well as optimized.
AGV Path Planning Using Curiosity-Driven Deep Reinforcement Learning

In the field of intelligent manufacturing, automated guided vehicles (AGV s) are required to plan an optimal path to a destination quickly and safely in complex environments. Several research papers have shown that Deep Reinforcement Learning (DRL) can plan a reasonable path for AGVs. However, almost all studies do not consider the influence of sparse external rewards. In addition, most research still uses an unrealistic 2D grid for their path planning environments. To alleviate these problems, we propose a novel AGV path planning method and provide the virtual environments of AGV s that approximate the real physical world. Moreover, the curiosity mechanism helps to enhance the Proximal Policy Optimization (PPO) method to provide additional intrinsic rewards to AGV agents in the sparse reward scenario. As for the environments, we no longer divide them into a 2D grid. Thus, the AGV has a continuous position in our environments. In addition, dynamic obstacles are added to make our environments more realistic. Based on our experiments, we make qualitative and quantitative analyses of the performance of the proposed method and the baseline DRL method. The empirical results show that our proposed method can make AGVs plan a reasonable and safe path with considerable speed during the training process in our virtual environments.
Learn from Robot: Transferring Skills for Diverse Manipulation via Cycle Generative Networks

Reinforcement learning (RL) has shown impressive results on a variety of robot tasks, but it requires a large amount of data for learning a single RL policy. However, in manufacturing there is a wide demand of reusing skills from different robots and it is hard to transfer the learned policy to different hardware due to diverse robot body morphology, kinematics, and dynamics. In this paper, we address the problem of transferring policies between different robot platforms. We learn a set of skills on each specific robot and represent them in a latent space. We propose to transfer the skills between different robots by mapping latent action spaces through a cycle generative network in a supervised learning manner. We extend the policy model learned on one robot with a pre-trained generative network to enable the robot to learn from the skill of another robot. We evaluate our method on several simulated experiments and demonstrate that our Learn from Robot (LfR) method accelerates new skill learning.
Development of an Autonomous Blower Robot for Cleaning up and Collecting Fallen Leaves

The use of household cleaning robots has become increasingly popular, but automation of cleaning up fallen leaves using a blower has not been studied. In this study, we develop an autonomous mobile robot that performs cleaning up leaves with a blower mounted on the robot. We adopt a three-wheel active caster as a moving mechanism: Both the drive and steering axes of each wheel are driven by motors, enabling omnidirectional motion. The blower is fixed to the robot with a slight downward tilt. We evaluated the quantity of leaves cleaned up from a large area varying the speed of movement. We found that the faster the robot moved, the fewer leaves were cleaned up, but at least 98% at the highest speed. We also evaluated the robot's ability to collect leaves using a wall as a guide. As a result, about half of the leaves were missed because the blower was not targeted at the landing place of the flown-up leaves. It turned out that we need to study the three-dimensional behavior of the jet stream for future work.
Optimization of Image Preprocessing and Background Influences using a Depth Camera for Person Re-Identification on a Mobile Robot

In this paper, we optimize datasets with different image preprocessing techniques for person re-identification using the ResNet18 model on a mobile robot with limited hardware, e.g., computational power and depth camera. For this, we create 16 datasets for which we discovered that the inverted original images, from the IR gray value images of the depth camera, results in the highest values with an r1, r5 and mAP of 98.48 %,99.82 % and 80.86 %. Additionally, we explore the cross-dataset evaluation for the 16 datasets to examine the robustness of our model, which points to a low generalizability. The scores are associated with the similarity between the trained and evaluated dataset.
A Vector Field-Based Method for Human Action Representation and Recognition During Human-Robot Collaboration

This paper presents a vector field-based method for human action representation and recognition to query human hand motions in a human-robot shared workspace. A vector field of an action is fashioned to span the workspace domain with velocity vectors that evolve to encompass the dynamic trajectory of its motion as it traverses a shared workspace. This method facilitates comparison between known and prevailing actions with wide variations in starting positions and trajectory motion, enabling robust action recognition without an onerous training phase. Two preliminary experiments are conducted to validate the proposed method. The preliminary results show early and consistent action recognition with a success rate of up to 93.33 % when tested in the presence of a robot.
CNN-Based Task State Estimation for Safer Automation of Oxy-Fuel Metal Cutting

The industrial operation of oxy-fuel metal cutting via gas torches involves tasks such as ignition, preheating, and combustion along the target surface. Automated oxy-fuel cutting systems are exposed to risks and anomalies that can lead to incorrect actions and safety hazards. In this paper, we develop a classifier for online task state estimation to assess the cutting robot's actions, detect anomalies, and reduce the risk of hazards. Using representative footage from our robotic cutting experiments, we curate an image dataset labeled with four types of cutting task states. Using deep learning methods, we design and train a convolutional neural network model for classifying the cutting task state from input images. The classifier architecture is optimized for rapid inferences during online estimation. After evaluation, our classifier achieves an overall accuracy of 93.8 % with high inference speeds on two types of representative hardware. Our ‘Oxy-fuel Cutting Task State’ (OCTS) dataset is available at doi.org/10.5281/zenodo.7734951.
An Improved Oversampling Algorithms based on Informative Sample Selection Strategy Solving Imbalance

Imbalanced data has been the focus of ongoing classification research. It describes a scenario where the distribution of data samples is uneven, and one or more classes in the dataset are underrepresented as a result. When trained on such datasets, this mismatch has a negative impact on the performance of conventional learning models. The key problem is in finding appropriate samples for creating synthetic data, even though numerous strategies have been developed to overcome class imbalance during data pre-processing. In this study, we offer an efficient method for overcoming imbalance classification issues caused by oversampling called Informative Sample Selection (ISS). The main goal of ISS is to find useful samples from the minority class in the dataset that may be used to produce data that is synthetic. We conducted experiments on 22 imbalanced datasets to evaluate the performance of our suggested model. We assessed the performance of ISS in comparison to several cutting-edge techniques, including SMOTE, Borderline-SMOTE, ADASYN, safe-level SMOTE, and ROS. AUC and F-Measure were the evaluation measures employed in our study. The outcomes of our tests show that ISS works better than the current approaches, showing significant progress in tackling the challenges brought on by imbalanced data in classification.