Hasil untuk "Computer software"

George Forman, J. Zahorjan

Justine Cassell

K. Wolter

T. Swinscow, M. Campbell

H. Hou, H. Andrews

M. Hsueh, T. Tsai, R. Iyer

T. Isakowitz, E. Stohr, P. Balasubramanian

D. F. Rogers, J. A. Adams

L. Bergasa, J. Nuevo

Udit Gupta, Young Geun Kim, Sylvia Lee et al.

Given recent algorithm, software, and hardware innovation, computing has enabled a plethora of new applications. As computing becomes increasingly ubiquitous, however, so does its environmental impact. This paper brings the issue to the attention of computer-systems researchers. Our analysis, built on industry-reported characterization, quantifies the environmental effects of computing in terms of carbon emissions. Broadly, carbon emissions have two sources: operational energy consumption, and hardware manufacturing and infrastructure. Although carbon emissions from the former are decreasing thanks to algorithmic, software, and hardware innovations that boost performance and power efficiency, the overall carbon footprint of computer systems continues to grow. This work quantifies the carbon output of computer systems to show that most emissions related to modern mobile and data-center equipment come from hardware manufacturing and infrastructure. We therefore outline future directions for minimizing the environmental impact of computing systems.

Kai Hwang

S. Pepke, B. Wold, A. Mortazavi

J. Demme, Matthew Maycock, J. Schmitz et al.

Armin Biere, Marijn J. H. Heule, H. Maaren et al.

Tanja E. J. Vos, Tijs van der Storm, Alexander Serebrenik et al.

Software engineering is the invisible infrastructure of the digital age. Every breakthrough in artificial intelligence, quantum computing, photonics, and cybersecurity relies on advances in software engineering, yet the field is too often treated as a supportive digital component rather than as a strategic, enabling discipline. In policy frameworks, including major European programmes, software appears primarily as a building block within other technologies, while the scientific discipline of software engineering remains largely absent. This position paper argues that the long-term sustainability, dependability, and sovereignty of digital technologies depend on investment in software engineering research. It is a call to reclaim the identity of software engineering.

Hongyu Lin, Samer Abdallah, Makar Valentinov et al.

Large Language Models (LLMs) have shown strong performance on code understanding tasks, yet they fundamentally lack the ability to perform precise, exhaustive mathematical reasoning about program behavior. Existing benchmarks either focus on mathematical proof automation, largely disconnected from real-world software, or on engineering tasks that do not require semantic rigor. We present CodeLogician, a neurosymbolic agent for precise analysis of software logic, integrated with ImandraX, an industrial automated reasoning engine deployed in financial markets and safety-critical systems. Unlike prior approaches that use formal methods primarily to validate LLM outputs, CodeLogician uses LLMs to construct explicit formal models of software systems, enabling automated reasoning to answer rich semantic questions beyond binary verification outcomes. To rigorously evaluate mathematical reasoning about software logic, we introduce code-logic-bench, a benchmark targeting the middle ground between theorem proving and software engineering benchmarks. It measures reasoning correctness about program state spaces, control flow, coverage constraints, and edge cases, with ground truth defined via formal modeling and region decomposition. Comparing LLM-only reasoning against LLMs augmented with CodeLogician, formal augmentation yields substantial improvements, closing a 41-47 percentage point gap in reasoning accuracy. These results demonstrate that neurosymbolic integration is essential for scaling program analysis toward rigorous, autonomous software understanding.

SHA Yuyang, LU Jingtao, DU Haofan, ZHAI Xiaobing, MENG Weiyu, LIAN Xu, LUO Gang, LI Kefeng

Image segmentation is a crucial technology for environmental perception, and it is widely used in various scenarios such as autonomous driving and virtual reality. With the rapid development of technology, computer vision-based blind guiding systems are attracting increasing attention as they outperform traditional solutions in terms of accuracy and stability. The semantic segmentation of road images is an essential feature of a visual guiding system. By analyzing the output of algorithms, the guiding system can understand the current environment and aid blind people in safe navigation, which helps them avoid obstacles, move efficiently, and get the optimal moving path. Visual blind guiding systems are often used in complex environments, which require high running efficiency and segmentation accuracy. However, commonly used high-precision semantic segmentation algorithms are unsuitable for use in blind guiding systems owing to their low running speed and a large number of model parameters. To solve this problem, this paper proposes a lightweight road image segmentation algorithm based on multiscale features. Unlike existing methods, the proposed model contains two feature extraction branches, namely, the Detail Branch and Semantic Branch. The Detail Branch extracts low-level detail information from the image, while the Semantic Branch extracts high-level semantic information. Multiscale features from the two branches are processed and used by the designed feature mapping module, which can further improve the feature modeling performance. Subsequently, a simple and efficient feature fusion module is designed for the fusion of features with different scales to enhance the ability of the model in terms of encoding contextual information by fusing multiscale features. A large amount of road segmentation data suitable for blind guiding scenarios are collected and labeled, and a corresponding dataset is generated. The model is trained and tested on the dataset. The experimental results show that the mean Intersection over Union (mIoU) of the proposed method is 96.5%, which is better than that of existing image segmentation models. The proposed model can achieve a running speed of 201 frames per second on NVIDIA GTX 3090Ti, which is higher than that of existing lightweight image segmentation models. The model can be deployed on NVIDIA AGX Xavier to obtain a running speed of 53 frames per second, which can meet the requirements for practical applications.

Miia Martikainen, Kari Smolander, Johan Sanmark et al.

Abstract BackgroundGenerative artificial intelligence (GAI) is expected to enhance the productivity of the public social and health care sector while maintaining, at minimum, current standards of quality and user experience. However, empirical evidence on GAI impacts in practical, real-life settings remains limited. ObjectiveThis study investigates productivity, machine translation quality, and user experience impacts of the GPT-4 language model in an in-house language translation services team of a large well-being services county in Finland. MethodsA mixed methods study was conducted with 4 in-house translators between March and June 2024. Quantitative data of 908 translation segments were collected in real-life conditions using the computer-assisted language translation software Trados (RWS) to assess productivity differences between machine and human translation. Quality was measured using 4 automatic metrics (human-targeted translation edit rate, Bilingual Evaluation Understudy, Metric for Evaluation of Translation With Explicit Ordering, and Character n-gram F-score) applied to 1373 GAI-human segment pairs. User experience was investigated through 5 semistructured interviews, including the team supervisor. ResultsThe findings indicate that, on average, postediting machine translation is 14% faster than translating texts from scratch (2.75 vs 2.40 characters per second, P ConclusionsBased on this case study, GPT-4–based GAI shows measurable potential to enhance translation productivity and quality within an in-house translation team in the public social and health care sector. However, its effectiveness appears to be influenced by factors, such as translator postediting skills, workflow design, and organizational readiness. These findings suggest that, in similar contexts, public social and health care organizations could benefit from investing in translator training, optimizing technical integration, redesigning workflows, and implementing effective change management. Future research should examine larger translator teams to assess the generalizability of these results and further explore how translation quality and user experience can be improved through domain-specific customization.

Nils Quetschlich, Olivia Di Matteo

As quantum computers continue to improve in quality and scale, there is a growing need for accessible software frameworks for programming them. However, the unique behavior of quantum systems means specialized approaches, beyond traditional software development, are required. This is particularly true for debugging due to quantum bugs, i.e., bugs that occur precisely because an algorithm is a quantum algorithm. Pinpointing a quantum bug's root cause often requires significant developer time, as there is little established guidance for quantum debugging techniques. Developing such guidance is the main challenge we sought to address. In this work, we describe a set of 14 quantum bugs, sourced primarily from our experience as quantum software developers, and supplemented by analysis of open-source GitHub repositories. We detail their context, symptoms, and the techniques applied to identify and fix them. While classifying these bugs based on existing schemes, we observed that most emerged due to unique interactions between multiple aspects of an algorithm or workflow. In other words, they occurred because more than one thing went wrong, which provided important insight into why quantum debugging is more challenging. Furthermore, based on this clustering, we found that - unexpectedly - there is no clear relationship between debugging strategies and bug classes. Further research is needed to develop effective and systematic quantum debugging strategies.

Paris Avgeriou, Nauman bin Ali, Marcos Kalinowski et al.

Increasingly, courses on Empirical Software Engineering research methods are being offered in higher education institutes across the world, mostly at the M.Sc. and Ph.D. levels. While the need for such courses is evident and in line with modern software engineering curricula, educators designing and implementing such courses have so far been reinventing the wheel; every course is designed from scratch with little to no reuse of ideas or content across the community. Due to the nature of the topic, it is rather difficult to get it right the first time when defining the learning objectives, selecting the material, compiling a reader, and, more importantly, designing relevant and appropriate practical work. This leads to substantial effort (through numerous iterations) and poses risks to the course quality. This chapter attempts to support educators in the first and most crucial step in their course design: creating the syllabus. It does so by consolidating the collective experience of the authors as well as of members of the Empirical Software Engineering community; the latter was mined through two working sessions and an online survey. Specifically, it offers a list of the fundamental building blocks for a syllabus, namely course aims, course topics, and practical assignments. The course topics are also linked to the subsequent chapters of this book, so that readers can dig deeper into those chapters and get support on teaching specific research methods or cross-cutting topics. Finally, we guide educators on how to take these building blocks as a starting point and consider a number of relevant aspects to design a syllabus to meet the needs of their own program, students, and curriculum.