Hasil untuk "Computer software"

R. Herrmann

S. Rautaray, A. Agrawal

A. Edelstein, N. Amodaj, Karl Hoover et al.

J. Šimůnek, M. Genuchten, M. Šejna

P. Bourque, R. Fairley

W. Banzhaf, F. Francone, Robert E. Keller et al.

H. Grissino-Mayer

T. Austin, E. Larson, Dan Ernst

J. Freeman, N. Ambady

Karina Kohl, Luigi Carro

Software Engineering (SE) faces simultaneous pressure from AI automation (reducing code production costs) and hardware-energy constraints (amplifying failure costs). We position that SE must redefine itself around human discernment-intent articulation, architectural control, and verification-rather than code construction. This shift introduces accountability collapse as a central risk and requires fundamental changes to research priorities, educational curricula, and industrial practices. We argue that Software Engineering, as traditionally defined around code construction and process management, is no longer sufficient. Instead, the discipline must be redefined around intent articulation, architectural control, and systematic verification. This redefinition shifts Software Engineering from a production-oriented field to one centered on human judgment under automation, with profound implications for research, practice, and education.

Ian A. Cosden, Elizabeth Holtz, Joel U. Bretheim

Research Software Engineers (RSEs) have become indispensable to computational research and scholarship. The fast rise of RSEs in higher education and the trend of universities to be slow creating or adopting models for new technology roles means a lack of structured career pathways that recognize technical mastery, scholarly impact, and leadership growth. In response to an immense demand for RSEs at Princeton University, and dedicated funding to grow the RSE group at least two-fold, Princeton was forced to strategize how to cohesively define job descriptions to match the rapid hiring of RSE positions but with enough flexibility to recognize the unique nature of each individual position. This case study describes our design and implementation of a comprehensive RSE career ladder spanning Associate through Principal levels, with parallel team-lead and managerial tracks. We outline the guiding principles, competency framework, Human Resources (HR) alignment, and implementation process, including engagement with external consultants and mapping to a standard job leveling framework utilizing market benchmarks. We share early lessons learned and outcomes including improved hiring efficiency, clearer promotion pathways, and positive reception among staff.

Himon Thakur, Armin Moin

The discussion around AI-Engineering, that is, Software Engineering (SE) for AI-enabled Systems, cannot ignore a crucial class of software systems that are increasingly becoming AI-enhanced: Those used to enable or support the SE process, such as Computer-Aided SE (CASE) tools and Integrated Development Environments (IDEs). In this paper, we study the energy efficiency of these systems. As AI becomes seamlessly available in these tools and, in many cases, is active by default, we are entering a new era with significant implications for energy consumption patterns throughout the Software Development Lifecycle (SDLC). We focus on advanced Machine Learning (ML) capabilities provided by Large Language Models (LLMs). Our proposed approach combines Retrieval-Augmented Generation (RAG) with Prompt Engineering Techniques (PETs) to enhance both the quality and energy efficiency of LLM-based code generation. We present a comprehensive framework that measures real-time energy consumption and inference time across diverse model architectures ranging from 125M to 7B parameters, including GPT-2, CodeLlama, Qwen 2.5, and DeepSeek Coder. These LLMs, chosen for practical reasons, are sufficient to validate the core ideas and provide a proof of concept for more in-depth future analysis.

Soojeong Lee, Gyanendra Prasad Joshi, Gangseong Lee

Abstract Rapid respiratory rate (RR) changes in older adults may indicate serious illness. Therefore, accurately estimating RR for cardiorespiratory fitness is essential. However, machine learning algorithm-related errors are unsuitable for medical decision-making processes because some data have a much larger sample size in the training set than in other sets. This difference in size refers to data imbalance. Therefore, we introduce a novel methodology combining bootstrap-based imbalanced feature generation (BIFG) with the Gaussian process for estimating RR and uncertainty, thereby addressing data imbalance. The sample difference between normal breathing (12–20 bpm), dyspnea ( $$\ge$$ 20 bpm), and hypopnea (<8 bpm) indicates significant data imbalance, which can affect the learning of the machine learning algorithm. Thus, the normal breathing part with much data is well-trained. The dyspnea and hypopnea parts with relatively little data are not well-trained, and this data imbalance causes significant errors concerning the reference variables in the actual dyspnea and hypopnea data parts. Hence, we use the parametric bootstrap model generated by artificial feature curves to estimate RR and solve this problem. As a result, the non-parametric bootstrap approach drastically increased the number of artificial feature curves. The generated artificial feature curves are selectively utilized for the highly imbalanced parts. Therefore, BIFG can be efficiently trained to predict the complex nonlinear relationships between the feature vectors obtained from the photoplethysmography signals and the reference RR. The proposed methodology exhibits more accurate predictive performance and uncertainty. The mean absolute errors are 0.89 and 1.44 beats per minute for RR using the proposed BIFG based on the two data sets.

Hans Djalali, Wajdi Aljedaani, Stephanie Ludi

This study investigates how software developers discuss usability on Stack Overflow through an analysis of posts from 2008 to 2024. Despite recognizing the importance of usability for software success, there is a limited amount of research on developer engagement with usability topics. Using mixed methods that combine quantitative metric analysis and qualitative content review, we examine temporal trends, comparative engagement patterns across eight non-functional requirements, and programming context-specific usability issues. Our findings show a significant decrease in usability posts since 2010, contrasting with other non-functional requirements, such as performance and security. Despite this decline, usability posts exhibit high resolution efficiency, achieving the highest answer and acceptance rates among all topics, suggesting that the community is highly effective at resolving these specialized questions. We identify distinctive platform-specific usability concerns: web development prioritizes responsive layouts and form design; desktop applications emphasize keyboard navigation and complex controls; and mobile development focuses on touch interactions and screen constraints. These patterns indicate a transformation in the sharing of usability knowledge, reflecting the maturation of the field, its integration into frameworks, and the migration to specialized communities. This first longitudinal analysis of usability discussions on Stack Overflow provides insights into developer engagement with usability and highlights opportunities for integrating usability guidance into technical contexts.

Neil He, Ming-Cheng Cheng, Yu Liu

The rising demand for high-performance computing (HPC) has made full-chip dynamic thermal simulation in many-core GPUs critical for optimizing performance and extending device lifespans. Proper orthogonal decomposition (POD) with Galerkin projection (GP) has shown to offer high accuracy and massive runtime improvements over direct numerical simulation (DNS). However, previous implementations of POD-GP use MPI-based libraries like PETSc and FEniCS and face significant runtime bottlenecks. We propose a PyTorch-based POD-GP library (PyPOD-GP), a GPU-optimized library for chip-level thermal simulation. PyPOD-GP achieves over 23.4× speedup in training and over 10× speedup in inference on a GPU with over 13,000 cores, with just 1.2% error over the device layer.

XIAO Chaoen, LI Zifan, ZHANG Lei, WANG Jianxin, QIAN Siyuan

In differential analysis-based cryptographic attacks, Bayesian optimization is typically used to verify whether the partially decrypted data exhibit differential characteristics. Currently, the primary approach involves training a differential distinguisher using deep learning techniques. However, this method has a notable limitation in that, as the number of encryption rounds increases, the accuracy of the differential characteristics decreases linearly. Therefore, a new differential characteristic discrimination method is proposed based on the attention mechanism and side-channel analysis. Using the difference relationship between multiple rounds of the ciphertext, a difference partition for the SPECK32/64 algorithm is trained based on the transformer. In a key recovery attack, a novel scheme is designed based on the previous ciphertext treatment to distinguish the most influential features of the ciphertext. In the key recovery attack of the SPECK32/64 algorithm, 2⁶ selected ciphertext pairs are used. Using the 20th round ciphertext pairs, the 65 536 candidate keys of the 22nd round can be screened within 17 on average, and the key recovery attack of the last two wheels can be completed. The experimental results show that this method achieves a success rate of 90%, effectively addressing the challenge of recognizing ciphertext differential features caused by an increase in the number of encryption rounds.

François Siewe, Guy Merlin Ngounou

The unified modelling language (UML) is an industrial de facto standard for system modelling. It consists of a set of graphical notations (also known as diagrams) and has been used widely in many industrial applications. Although the graphical nature of UML is appealing to system developers, the official documentation of UML does not provide formal semantics for UML diagrams. This makes UML unsuitable for formal verification and, therefore, limited when it comes to the development of safety/security-critical systems where faults can cause damage to people, properties, or the environment. The UML activity diagram is an important UML graphical notation, which is effective in modelling the dynamic aspects of a system. This paper proposes a formal semantics for UML activity diagrams based on the calculus of context-aware ambients (CCA). An algorithm (semantic function) is proposed that maps any activity diagram onto a process in CCA, which describes the behaviours of the UML activity diagram. This process can then be executed and formally verified using the CCA simulation tool ccaPL and the CCA runtime verification tool ccaRV. Hence, design flaws can be detected and fixed early during the system development lifecycle. The pragmatics of the proposed approach are demonstrated using a case study in e-commerce.

Davide Fucci, Massimiliano Di Penta, Simone Romano et al.

Software Bills of Material (SBOMs) are becoming a consolidated, often enforced by governmental regulations, way to describe software composition. However, based on recent studies, SBOMs suffer from limited support for their consumption and lack information beyond simple dependencies, especially regarding software vulnerabilities. This paper reports the results of a preliminary study in which we augmented SBOMs of 40 open-source projects with information about Common Vulnerabilities and Exposures (CVE) exposed by project dependencies. Our augmented SBOMs have been evaluated by submitting pull requests and by asking project owners to answer a survey. Although, in most cases, augmented SBOMs were not directly accepted because owners required a continuous SBOM update, the received feedback shows the usefulness of the suggested SBOM augmentation.

Prakash Murali, N. Linke, M. Martonosi et al.

In recent years, Quantum Computing (QC) has progressed to the point where small working prototypes are available for use. Termed Noisy Intermediate-Scale Quantum (NISQ) computers, these prototypes are too small for large benchmarks or even for Quantum Error Correction (QEC), but they do have sufficient resources to run small benchmarks, particularly if compiled with optimizations to make use of scarce qubits and limited operation counts and coherence times. QC has not yet, however, settled on a particular preferred device implementation technology, and indeed different NISQ prototypes implement qubits with very different physical approaches and therefore widely-varying device and machine characteristics. Our work performs a full-stack, benchmark-driven hardware- software analysis of QC systems. We evaluate QC architectural possibilities, software-visible gates, and software optimizations to tackle fundamental design questions about gate set choices, communication topology, the factors affecting benchmark performance and compiler optimizations. In order to answer key cross-technology and cross-platform design questions, our work has built the first top-to- bottom toolflow to target different qubit device technologies, including superconducting and trapped ion qubits which are the current QC front-runners. We use our toolflow, TriQ, to conduct real-system measurements on seven running QC prototypes from three different groups, IBM, Rigetti, and University of Maryland. Overall, we demonstrate that leveraging microarchitecture details in the compiler improves program success rate up to 28x on IBM (geomean 3x), 2.3x on Rigetti (geomean 1.45x), and 1.47x on UMDTI (geomean 1.17x), compared to vendor toolflows. In addition, from these real-system experiences at QC's hardware-software interface, we make observations and recommendations about native and software-visible gates for different QC technologies, as well as communication topologies, and the value of noise-aware compilation even on lower-noise platforms. This is the largest cross-platform real-system QC study performed thus far; its results have the potential to inform both QC device and compiler design going forward.

Afzaal Hussain, Muhammad Adeel Zahid, Usama Ahmed et al.

Machine learning-based sports activity recognition has captured a lot of interest in recent years. Automatic activity recognition not only reduces cost and time but is very helpful in analyzing health-sensitive data acquired using smart wearable technology. Gym activity recognition by incorporating smart wearable technology comes within the scope of this topic. This paper present a system for classifying gym activities using feature engineering techniques applied to time series data. The collected time series data consists of an athlete&#x2019;s body movement using an internal 3-axis accelerometer build into the zephyr bio-harness 3 device. The data were gathered by implementing a six-week fitness routine trying to target six muscle groups, preceded by one day of rest and recovery each week. The raw time-series data of the accelerometer is transformed to extract new features from it for identifying gym activities. The feature engineering techniques applied in this research are not limited to gym activity recognition but can be extended to any domain involving time-series data. The collected data was just three features, which are the reading of the tri-axial accelerometer signal as vertical, lateral, and sagittal axes. In order to formulate new features, basic concepts of statistics and mathematics were applied to the data. furthermore, we trained six GridSearchCV-based classifiers on the extracted features and tested their performance in four different types of experiments.