Reviewerly/exHarmony huggingface dataset:
Prompts Matter: Insights and Strategies for Prompt Engineering in Automated Software Traceability

Large Language Models (LLMs) have the potential to revolutionize automated traceability by overcoming the challenges faced by previous methods and introducing new possibilities. However, the optimal utilization of LLMs for automated traceability remains unclear. This paper explores the process of prompt engineering to extract link predictions from an LLM. We provide detailed insights into our approach for constructing effective prompts, offering our lessons learned. Additionally, we propose multiple strategies for leveraging LLMs to generate traceability links, improving upon previous zero-shot methods on the ranking of candidate links after prompt refinement. The primary objective of this paper is to inspire and assist future researchers and engineers by highlighting the process of constructing traceability prompts to effectively harness LLMs for advancing automatic traceability.
Network Parameter Control in Cellular Networks through Graph-Based Multi-Agent Constrained Reinforcement Learning

Cellular networks are growing in complexity at increasing speed and the geographical locations in which they are deployed in are getting denser. Traditional control methods fall short in providing a scalable and dynamic way of adapting the network to new conditions. Distributed multiagent reinforcement learning successfully addresses scalability problems seen in centralized approaches. The question of achieving learning with constraint satisfaction in distributed systems is still left unanswered in the state-of-the-art. In this work, we aim to perform distributed multi-agent constrained reinforcement learning in order to learn a policy online while satisfying imposed constraints. We use a coordination graph to model the interactions between agents and decompose the global value function. A conservative safety critic is trained in parallel to evaluate the safety of proposed actions. Our method allows for separate training of both the critic and the value network independently of each other, and hence offers flexibility in how and when to train the different models. The results are compared to a baseline using no safety critic. Simulations show that the agents succeed in learning a policy that can satisfy the constraints, while still maximizing the objective.
Bi-Level Nonstationary Kernels for Online Gaussian Process Regression

In this work, we consider realtime mapping of spatial phenomena using Gaussian processes (GP). For the case that the spatial field changes in variability over different areas, stationary GPs whose kernel hyperparameters are constant may be insufficient in capturing local behaviors. To accurately model and represent data with evolving variability, we propose to use a Gibbs kernel where the length scale is allowed to vary over space. The length scale is modeled as a secondary Gaussian process, and we formulate a maximum a posteriori solution to estimate training values for the length scale GP. Combining this kernel with online sparse methods and nearest neighbor approximations allows us to control computational complexity involved in training and inference, making our approach suitable for real-time applications. The resulting bi-level Gaussian process framework efficiently processes streaming data and maintains good prediction accuracy as measured by standardized mean squared error. Empirical analysis on a bathymetric dataset that has been acquired by an autonomous underwater vehicle suggests that our framework produces comparable predictive performance with respect to recent online GP regression methods while maintaining acceptable computational speed for streaming applications.
Application of the Sustainability Awareness Framework in Agile Software Development

Sustainability has become a widely discussed topic in software and requirements engineering. However, in the context of agile software development processes, little is known about how to identify potential sustainability effects of software systems. The goal of our research is to investigate how requirements engineering knowledge on sustainability can be incorporated into agile frameworks like Scrum. In this experience paper, we present the results of two case studies from ongoing agile development projects where the Sustainability Awareness Framework was applied. Within workshops, more than 20 potential sustainability effects have been identified for each of the systems based on their product visions. Furthermore, we analyzed the product backlogs and found that more than half of the backlog items of both systems can be related to at least one of the identified sustainability effects. This suggests that a sustainability analysis of backlog items can support the early identification of potential sustainability effects in Scrum. Our findings suggest that the Sustainability Awareness Framework can be successfully used by practitioners in agile settings based on the guidelines provided by the Sustainability Awareness Framework.
Analysis and optimisation of SPL products using goal models

The Internet of Things is one of the core drivers of variability modelling and requires explicit mechanisms to manage it. A key technology for addressing this variability is product line engineering. This approach uses a reference architecture to establish a well-designed set of assets that fit together, the Software Product Line (SPL). One of the limitations of variability models is they do not provide information about the quality of new products or how they achieve stakeholder requirements. Several approaches tackle this issue by integrating variability models with goal models. The main challenge is conciliating the different variability perspectives to make the joint use of both models possible without the loss of information or alterations to the models' semantics. In this work, we present a framework for analysing and optimising SPL products considering stakeholders' requirements that respects the semantics of both models. The framework is based on Integer Linear Programming (ILP), a field of mathematical programming. Variability and goal models are formalised as a set of linear constraints and are linked using mapping functions. As a proof of concept, we present a tool that takes both models and mapping functions to generate an ILP problem that can be solved using Matlab.
A Multimedia Approach to Problem Descriptions for Fine-Grained Detail Characterization

Requirements analysis has always been regarded as the starting point and focus of requirements engineering. The Problem Frames (PF) approach provides a problem-oriented requirements analysis method by describing the software's operating environment in detail. It can effectively bridge the gap between requirements analysis and system design. However, an intuitive and convenient means of elaborating the rich details of complex requirements and their contexts is missing from the literature. Currently, the main methods for describing requirements are through textual descriptions or formal language descriptions, which can result in the PF model containing a large amount of textual representations, and increasing the difficulty and time required for user understanding. In this paper, we present the PF2Detail, a tool that enables fine-grained descriptions of the PF model. Building upon the PF model construction, PF2Detail incorporates multimedia to describe complex requirements in context. Each node in the model can include multimedia, text, or formal language to provide detailed characterizations. Additionally, users can view detailed information about any node in the model and navigate the entire model, aiming to enhance the level of details in the PF model descriptions.
Frustum-LDGCNN: A High Efficient 3D Object Detection Network with Frustum Proposal

In the field of autonomous driving, the object detection task faces complex and diverse scenes during the driving process. Depending solely on an RGB image for object detection lacks security and stability. Therefore, this paper proposes a object detection method based on the fusion of RGB images and point clouds. This paper combines frustum fusion method with the Link Dynamic Graph Convolutional Neural Network (LDGCNN) to propose a Frustum-LDGCNN 3D target detection method. LDGCNN can learn the spatial features between point clouds, which allows the network to more accurately locate object. In order to verify the performance of the algorithm, multiple comparative experiments were conducted on the KITTI dataset. Compared with other 3D object detection method, Frustum-LDGCNN has batter performance on the detection accuracy of cars, pedestrians, and bicycles.
Leveraging Natural Language Processing for a Consistency Checking Toolchain of Automotive Requirements

In the automotive industry, specifications often consist of a large number of textual requirements. These requirements are linguistically ambiguous and written in informal language. Utilizing Structured English for requirements eliminates ambiguity, improves data quality, and supports further automated processing while maintaining readability. The recent development of large language models enables a fully automated translation approach using few-shot learning. To deal with the limited context size of large language models, an improved algorithm, OptKATE, is presented to find an ideal set of requirements for few-shot learning. Structured English can be used as a basis for further formalization. This capability is key in creating an interface between natural language processing and verification, in our case, consistency analysis using the Z3 SMT solver. We implemented a grammar for translating Structured English into TCTL using the MontiCore workbench. Furthermore, since SMT-based methods currently rely on manual precondition satisfaction and do not tackle conflicting preconditions automatically, we propose a scenario generation algorithm that generates potential scenarios using the specification and checks the requirements against them. Through this approach, we can better identify and resolve conflicting preconditions, ultimately improving the consistency of requirements. Our toolchain is evaluated using an automotive requirements dataset provided by former Daimler AG.
LIFEDATA - A Framework for Traceable Active Learning Projects

Active Learning has become a popular method for iteratively improving data-intensive Artificial Intelligence models.However, it often presents a significant challenge when dealing with large volumes of volatile data in projects, as with an Active Learning loop.This paper introduces LIFEDATA, a Pythonbased framework designed to assist developers in implementing Active Learning projects focusing on traceability.It supports seamless tracking of all artifacts, from data selection and labeling to model interpretation, thus promoting transparency throughout the entire model learning process and enhancing error debugging efficiency while ensuring experiment reproducibility.To showcase its applicability, we present two life science use cases.Moreover, the paper proposes an algorithm that combines query strategies to demonstrate LIFEDATA's ability to reduce data labeling effort.
PID-YOLOX: An X-Ray Prohibited Items Detector Based on YOLOX

X-ray prohibited items detection is an effective and crucial measure in various security inspection scenarios. However, the overlapping phenomenon in X-ray images exacerbates the foreground-background class imbalance, and the imaging principle of X-ray images results in missing texture features. To address these challenges, we propose an end-to-end X-ray Prohibited Items Detector (PID-YOLOX) based on YOLOX-Tiny, which offers fast detection speed and high accuracy. Specifically, we introduce the Generalized Label Assignment (GLA) scheme to tackle the foreground-background class imbalance problem, and the Multi-Cardinality Attention (MCA) mechanism to alleviate the issue of missing texture features. Our experimental results show that PID-YOLOX achieves 54.9% average precision (AP) on the PIXray dataset, surpassing YOLOX-Tiny by 2.2% AP. Furthermore, extensive experiments demonstrate that PID-YOLOX is superior to the state-of-the-art methods, indicating its potential applications in the prohibited items detection field.
Design of an Advanced System-on-Chip Architecture for Chaotic Image Encryption

With the rise of interconnected systems, security has become a crucial concern. As a result, there has been a growing interest in developing low-cost embedded cryptographic algorithms that are lightweight and can be integrated into System-on-Chip (SoC) devices. Digital chaotic systems have emerged as a promising approach for building secure communication systems, where various cryptosystems utilize chaotic dynamics to encrypt images into noise-like representations. Recent studies have demonstrated that finite-precision error can be used to derive chaos, which can be systematically applied to encrypt images. However, there is a lack of research on how SoC-based design constraints affect the overall performance of image encryption systems, especially for applications that are sensitive to latency. This study aims to address this gap by presenting an efficient architecture that explores a hardware/software co-design for image encryption based on finite-precision error, specifically designed for resource-limited devices and latency-sensitive applications. Our platform performs the capture and encryption of images with a size of 320 × 240. Using a benchmark image, results show that the developed cryptosystem architecture can encrypt images efficiently while offering low hardware occupation.
An Anthropomorphic Framework for Learning-Based Visual Servoing to Reach Unseen Objects

This paper presents a novel biologically-inspired visual servoing framework that enables a robot arm to reach previously unseen objects without requiring robot or camera calibration. Our framework consists of an “eye” that estimates the approximate position of the target object, and a “brain” that guides the robot arm to reach the object through iterations of eye estimation and action. To this end, we train an estimator to estimate the position difference between the target view and the current view in an “eye-in-hand” setting. Furthermore, we introduce a reinforcement learning-based policy network to guide the robot arm with the estimation information. Our experimental results demonstrate that the proposed framework is plausible, and has the potential for real-world deployment due to its ability to reduce the observation space and accelerate the training process.
Visualizing Software Repositories Through Requirements Trace Links

Tracking the status of the requirements throughout the software development cycle is essential to the success of software development projects. Requirements trace links relate requirements with other software development artifacts, indicating the progress on their related requirements. This paper presents a tool that embeds different automated traceability methods and extracts trace links from requirements to issues, pull requests, and commits using keyword matching, TF-IDF vectors, and word vectors. Extracted links are used to create an interactive visualization of the repository in a dashboard for retrospective and real-time analysis. This visualization benefits the managers to track the progress of a project both during or after the development phase and identify the contribution of the developers. Additionally, software engineering educators and students may utilize the visualization of the traces as an indicator of the quality of the requirements and other software development artifacts by outlining the successful or problematic pieces. Our main contribution is the interactive dashboard that utilizes trace links to visualize a software repository to support project management and analysis. We present a preliminary evaluation of the underlying trace link extraction methods and lay out the details of our future evaluation plan. Our replication package contains the code and the evaluation data.
Requirements Classification for Smart Allocation: A Case Study in the Railway Industry

Allocation of requirements to different teams is a typical preliminary task in large-scale system development projects. This critical activity is often performed manually and can benefit from automated requirements classification techniques. To date, limited evidence is available about the effectiveness of existing machine learning (ML) approaches for requirements classification in industrial cases. This paper aims to fill this gap by evaluating state-of-the-art language models and ML algorithms for classification in the railway industry. Since the interpretation of the results of ML systems is particularly relevant in the studied context, we also provide an information augmentation approach to complement the output of the ML-based classification. Our results show that the BERT uncased language model with the softmax classifier can allocate the requirements to different teams with a 76% F1 score when considering requirements allocation to the most frequent teams. Information augmentation provides potentially useful indications in 76% of the cases. The results confirm that currently available techniques can be applied to real-world cases, thus enabling the first step for technology transfer of automated requirements classification. The study can be useful to practitioners operating in requirements-centered contexts such as railways, where accurate requirements classification becomes crucial for better allocation of requirements to various teams.
Penetration State Recognition for GMAW Process Using Stereo Vision Based on Deep Learning

As a major arc welding process, pulsed gas metal arc welding (GMAW-P) is extensively applied in the production of various critical components due to its benefits such as high weld strength and ease of automation. The geometry of the weld pool contains a lot of welding quality characteristics in the process of welding, which is an important basis for judging the penetration states of the weld pool. For such key features, we develop a single camera stereo vision system based on a biprism and propose a deep learning-based penetration states recognition strategy. Using a stereo matching network based on attention concatenation volume, filter redundant information in concatenation volumes by generating attention weights to predict the accurate disparity map, and compare experimental results to evaluate its validity in weld pool disparity estimation. Secondly, to train the penetration states recognition model, a depth-wise separable convolutions neural network is built. This model directly maps the weld pool penetration relationship to disparity without any preprocessing and extracts deeper features such as pool contours and depth of fusion, then accurately identify the penetration states. According to the results of the experiments, based on disparity characteristics, penetration states classification accuracy is 99.83%.
Handling Uncertainty in the Development of Conversational Assistants with RELAXed User Stories

Conversational assistants have become increasingly popular, being used in different application domains, from Smart Homes to Health Care. Nevertheless, a considerable challenge concerning their development is to deal with their intrinsic uncertainty. Here we want to focus on the development early stages of this kind of system. Thus, to address uncertainty and simultaneously keep the documentation easy to maintain and user-centered, we opted to specify the requirements by combining the RELAX approach and user stories, creating the RELAXed User Stories approach. To accomplish this, we started by specifying a metamodel that expresses the concepts and relationships between user stories and RELAX. Also, a template to specify a RELAXed User Story is proposed. The main contribution is to help the requirements engineer work to specify conversational assistants, where uncertainty plays a significant role, in particular in the agile development context where user stories are common artifacts. We illustrate the use of RELAXed User Stories with a simple example. Finally, we performed a preliminary qualitative evaluation of the approach, with encouraging results.
Generating Requirements Elicitation Interview Scripts with Large Language Models

Requirements elicitation interviews are the most popular requirements elicitation technique and an integral part of requirements engineering education. Good and bad interview scripts provide students with examples of applying the theory. Constructing an interview script requires technical knowledge, practical experience, and creativity. As a result, only a few educational interview scripts are available to the community. This paper explores automatically generating interview scripts with large language models through prompt engineering. Our contribution is two-fold: First, we present a graph representation of interactive interview scripts. Second, we apply prompt engineering techniques to generate business domain descriptions, linear scripts, and conversation pieces focused on certain types of mistakes. Our findings indicate that large language models face challenges in handling interview conversation graphs. However, we can enhance the quality of the generated interview scripts by decomposing the task into smaller components and refining the prompts to provide more precise instructions.
Reinforcement Learning Based Black-Box Adversarial Attack for Robustness Improvement

We propose a Reinforcement Learning (RL) based adversarial Black-box attack (RLAB) that aims at adding minimum distortion to the input iteratively to deceive image classification models. The RL agent learns to identify highly sensitive regions in the input's feature space to add distortions to induce misclassification with minimum steps and L2 norm. The agent also selectively removes noises introduced at earlier steps in the iteration, which has less impact on the model at a given state. This novel dual-action method is equivalent to doing a deep tree search to add noises without an exhaustive search, leading to the faster generation of an optimum adversarial sample. This black-box method focuses on naturally occurring distortion to effectively measure the robustness of models, a key element of trustworthiness. The proposed method beats existing heuristic based state-of-the-art black-box adversarial attacks on metrics such as the number of queries, L2 norm, and success rate on ImageNet and CIFAR-10 datasets. For the ImageNet dataset, the average number of queries achieved by the proposed method for ResNet-50, Inception-V3, and VGG-16 models are 42%, 32%, and 31 % better than the popular “Square Attack”. Furthermore, retraining the model with adversarial samples significantly improved robustness when evaluated on benchmark datasets such as CIFAR-10-C with the metrics of adversarial error and mean corruption error (mCE). Demo: https://tinyurl.com/yr8f7x9t
Towards Locating Requirements within Agile Development Artifacts

The increasing adoption of agile software development has led to a shift toward lightweight and informal requirements documentation. While creating prototypes for automating requirements analysis and management, researchers often overlook the flexibility of requirements documentation in agile settings. We aim to explore the gap between the assumptions that researchers make and real-world documentation practices. Since this gap causes challenges for locating requirements, we plan to develop a tool to help practitioners extract this information.