Hasil untuk "Computer software"

R. Sandy

Raul Medina-Mora, T. Winograd, Rodrigo Flores et al.

K. Yam, S. Papadakis

S. R. Hiltz, M. Turoff

C. Lawson

Daniel Genkin, A. Shamir, Eran Tromer

Alessio Gambi, Marc Müller, G. Fraser

Self-driving cars rely on software which needs to be thoroughly tested. Testing self-driving car software in real traffic is not only expensive but also dangerous, and has already caused fatalities. Virtual tests, in which self-driving car software is tested in computer simulations, offer a more efficient and safer alternative compared to naturalistic field operational tests. However, creating suitable test scenarios is laborious and difficult. In this paper we combine procedural content generation, a technique commonly employed in modern video games, and search-based testing, a testing technique proven to be effective in many domains, in order to automatically create challenging virtual scenarios for testing self-driving car soft- ware. Our AsFault prototype implements this approach to generate virtual roads for testing lane keeping, one of the defining features of autonomous driving. Evaluation on two different self-driving car software systems demonstrates that AsFault can generate effective virtual road networks that succeed in revealing software failures, which manifest as cars departing their lane. Compared to random testing AsFault was not only more efficient, but also caused up to twice as many lane departures.

Łukasz Szeremeta

Knows is a command-line property graphs generator for prototyping, testing, database development, and scientific or educational purposes. The tool emphasizes zero-configuration defaults with optional parameters for simple use cases, while also supporting optional schema files for custom graph structures. Knows exports to multiple formats (including YARS-PG, GraphML, CSV, Cypher, and JSON), includes a minimal built-in visualizer, and ensures reproducibility across formats via an optional random seed. The tool is widely available on PyPI and Docker Hub, and is ready for use by researchers, developers, educators, students, and anyone working with graph data.

Paloma Guenes, Rafael Tomaz, Maria Teresa Baldassarre et al.

The Impostor Phenomenon (IP) impacts a significant portion of the Software Engineering workforce, yet it is often viewed primarily through an internal individual lens. In this position paper, we propose framing the prevalence of IP as a form of Human Debt and discuss the relation with the ICSE2026 Pre Survey on the Future of Software Engineering results. Similar to technical debt, which arises when short-term goals are prioritized over long-term structural integrity, Human Debt accumulates due to gaps in psychological safety and inclusive support within socio-technical ecosystems. We observe that this debt is not distributed equally, it weighs heavier on underrepresented engineers and researchers, who face compounded challenges within traditional hierarchical structures and academic environments. We propose cultural refactoring, transparency and active maintenance through allyship, suggesting that leaders and institutions must address the environmental factors that exacerbate these feelings, ensuring a sustainable ecosystem for all professionals.

Niruthiha Selvanayagam, Taher A. Ghaleb, Manel Abdellatif

Self-admitted technical debt (SATD), referring to comments flagged by developers that explicitly acknowledge suboptimal code or incomplete functionality, has received extensive attention in machine learning (ML) and traditional (Non-ML) software. However, little is known about how SATD manifests and evolves in contemporary Large Language Model (LLM)-based systems, whose architectures, workflows, and dependencies differ fundamentally from both traditional and pre-LLM ML software. In this paper, we conduct the first empirical study of SATD in the LLM era, replicating and extending prior work on ML technical debt to modern LLM-based systems. We compare SATD prevalence across LLM, ML, and non-ML repositories across a total of 477 repositories (159 per category). We perform survival analysis of SATD introduction and removal to understand the dynamics of technical debt across different development paradigms. Surprisingly, despite their architectural complexity, our results reveal that LLM repositories accumulate SATD at similar rates to ML systems (3.95% vs. 4.10%). However, we observe that LLM repositories remain debt-free 2.4x longer than ML repositories (a median of 492 days vs. 204 days), and then start to accumulate technical debt rapidly. Moreover, our qualitative analysis of 377 SATD instances reveals three new forms of technical debt unique to LLM-based development that have not been reported in prior research: Model-Stack Workaround Debt, Model Dependency Debt, and Performance Optimization Debt. Finally, by mapping SATD to stages of the LLM development pipeline, we observe that debt concentrates

S. Mohurle, Manisha M. Patil

J. Musa

Fong Yee Lee, S Prabha Kumaresan, Elham Abdulwahab Anaam et al.

The issues with social media landscape are proliferation of disinformation, misinformation, and misinformation. The widespread of deepfakes makes is harder to distinguish between authentic content and fabricated content. The mediating effect of media literacy on news credibility has been understudied in previous research; the objective of the study is to investigate how much media literacy, news skepticism and fear of missing out (FOMO) influencing users' trust in the news disseminated on social media platforms. To achieve this, a survey was conducted to assess trust in and skepticism towards social media news, FOMO levels, and media literacy associated with deepfake news content. Educational efforts and media literacy initiatives are crucial in fostering informed and discerning news consumption. Furthermore, news organizations continue to prioritize transparency and accuracy to maintain credibility on social media since the news is easily accessible in the era of an information overload. The limitation of the study was the lack of assessment on evaluating effectiveness of media literacy in combating fabricated news content on social media. It is suggested to broaden scope by studying additional factors to combat fake news such as journalistic standards, fact-checking and verification are important to build reader’s trust. Future studies should also measure the effectiveness of media literacy initiatives ensure they really make a difference. The generalizability of future study can be strengthened with the inclusion of diverse age groups especially vulnerable populations.

LÜ Jingqin, HU Lang, LIANG Weinan, LI Guangli, ZHANG Hongbin

COVID-19 is an illness caused by a strain of the novel coronavirus. Existing COVID-19 imaging diagnostic models face challenges such as the lack of high-quality samples and insufficient exploration of inter-sample relationships. This paper proposes a novel model called Attention Distillation Contrastive Mutual Learning (ADCML) for COVID-19 diagnosis, to address these two issues. First, a progressive data augmentation strategy is constructed, which includes AutoAugment and sample filtering, and the lack of quality samples is proactively addressed by expanding the number of images and ensuring their quality. Second, the ADCML framework is built, which employs attention distillation to motivate two heterogeneous networks to learn from each other the pathological knowledge concerned with their attention. The implicit contrastive relationships among the diverse samples are then fully mined to improve the discriminative ability of the extracted features. Finally, a new adaptive model-fusion module is designed to fully mine the complementarity between the heterogeneous networks and complete the COVID-19 image diagnosis. The proposed model is validated on three publicly available datasets-including Computed Tomography (CT) and X-ray images-with accuracies of 89.69%, 98.16%, and 98.91%; F1 values of 88.62%, 97.58%, and 98.47%; and Area Under the Curve (AUC) values of 88.95%, 97.77%, and 98.90%, respectively. These results show that the ADCML model outperforms the mainstream baselines and has strong robustness, and that progressive data augmentation, attention distillation, and contrastive mutual learning form a type of joint force that promotes the final classification performance.

Guanyu Zhu, Shehryar Sikander, Elia Portnoy et al.

We study parallel fault-tolerant quantum computing for families of homological quantum low-density parity-check (LDPC) codes defined on 3-manifolds with constant or almost-constant encoding rate. We derive a generic formula for a transversal T gate on color codes defined on general 3-manifolds, which acts as collective non-Clifford logical ccz gates on any triplet of logical qubits with their logical-X membranes having a Z_{2} triple intersection at a single point. The triple-intersection number is a topological invariant, which also arises in the path integral of the emergent higher symmetry operator in a topological quantum field theory (TQFT): the Z_{2}^{3} gauge theory. Moreover, the transversal S gate of the color code corresponds to a higher-form symmetry in TQFT supported on a codimension-1 submanifold, giving rise to exponentially many addressable and parallelizable logical cz gates. A construction of constant-depth circuits of the above logical gates via cup-product cohomology operation is also presented for three copies of identical toric codes on arbitrary 3-manifolds. We have developed a generic formalism to compute the triple-intersection invariants for 3-manifolds, with the structure encoded into an interaction hypergraph which determines the logical gate property and also corresponds to the hypergraph magic state that can be injected into the code without distillation (“magic-state fountain”). We also study the scaling of the Betti number and systoles with volume for various 3-manifolds, which translates to the encoding rate and distance. We further develop three types of LDPC codes supporting such logical gates: (1) A quasi-hyperbolic code from the product of 2D hyperbolic surface and a circle, with almost-constant rate k/n=O(1/log⁡(n)) and O(log⁡(n)) distance; (2) A homological fiber-bundle code from twisting the product by an isometry of the surface based on the construction by Freedman-Meyer-Luo, with O(1/log^{1/2}⁡(n)) rate and O(log^{1/2}⁡(n)) distance; (3) A specific family of 3D hyperbolic codes: the Torelli mapping-torus code, constructed from mapping tori of a pseudo-Anosov element in the Torelli subgroup, which has constant rate while the distance scaling is currently unknown. We then show a generic constant-overhead scheme for applying a parallelizable universal gate set with the aid of logical-X measurements.

Daniel Strassler, Gabe Elkin, Curran Schiefelbein et al.

Software plays an ever increasing role in complex system development and prototyping, and in recent years, MIT Lincoln Laboratory has sought to improve both the effectiveness and culture surrounding software engineering in execution of its mission. The Homeland Protection and Air Traffic Control Division conducted an internal study to examine challenges to effective and efficient research software development, and to identify ways to strengthen both the culture and execution for greater impact on our mission. Key findings of this study fell into three main categories: project attributes that influence how software development activities must be conducted and managed, potential efficiencies from centralization, opportunities to improve staffing and culture with respect to software practitioners. The study delivered actionable recommendations, including centralizing and standardizing software support tooling, developing a common database to help match the right software talent and needs to projects, and creating a software stakeholder panel to assist with continued improvement.

Rodrigo Oliveira Zacarias, Rodrigo Pereira dos Santos, Patricia Lago

Software ecosystems (SECO) have become a dominant paradigm in the software industry, enabling third-party developers to co-create value through complementary components and services. While Developer Experience (DX) is increasingly recognized as critical for sustainable SECO, transparency remains an underexplored factor shaping how developers perceive and interact with ecosystems. Existing studies acknowledge transparency as essential for trust, fairness, and engagement, yet its relationship with DX has not been systematically conceptualized. Hence, this work aims to advance the understanding of transparency in SECO from a developer-centered perspective. To this end, we propose SECO-TransDX (Transparency in Software Ecosystems from a Developer Experience Perspective), a conceptual model that introduces the notion of DX-driven transparency. The model identifies 63 interrelated concepts, including conditioning factors, ecosystem procedures, artifacts, and relational dynamics that influence how transparency is perceived and constructed during developer interactions. SECO-TransDX was built upon prior research and refined through a Delphi study with experts from academia and industry. It offers a structured lens to examine how transparency mediates DX across technical, social, and organizational layers. For researchers, it lays the groundwork for future studies and tool development; for practitioners, it supports the design of trustworthy, developer-centered platforms that improve transparency and foster long-term engagement in SECO.

Paloma Guenes, Rafael Tomaz, Bianca Trinkenreich et al.

Research shows that more than half of software professionals experience the Impostor Phenomenon (IP), with a notably higher prevalence among women compared to men. IP can lead to mental health consequences, such as depression and burnout, which can significantly impact personal well-being and software professionals' productivity. This study investigates how IP manifests among software professionals across intersections of gender with race/ethnicity, marital status, number of children, age, and professional experience. Additionally, it examines the well-being of software professionals experiencing IP, providing insights into the interplay between these factors. We analyzed data collected through a theory-driven survey (n = 624) that used validated psychometric instruments to measure IP and well-being in software engineering professionals. We explored the prevalence of IP in the intersections of interest. Additionally, we applied bootstrapping to characterize well-being within our field and statistically tested whether professionals of different genders suffering from IP have lower well-being. The results show that IP occurs more frequently in women and that the prevalence is particularly high among black women as well as among single and childless women. Furthermore, regardless of gender, software engineering professionals suffering from IP have significantly lower well-being. Our findings indicate that effective IP mitigation strategies are needed to improve the well-being of software professionals. Mitigating IP would have particularly positive effects on the well-being of women, who are more frequently affected by IP.

Roberto Verdecchia, Justus Bogner

While mastered by some, good scientific writing practices within Empirical Software Engineering (ESE) research appear to be seldom discussed and documented. Despite this, these practices are implicit or even explicit evaluation criteria of typical software engineering conferences and journals. In this pragmatic, educational-first document, we want to provide guidance to those who may feel overwhelmed or confused by writing ESE papers, but also those more experienced who still might find an opinionated collection of writing advice useful. The primary audience we had in mind for this paper were our own BSc, MSc, and PhD students, but also students of others. Our documented advice therefore reflects a subjective and personal vision of writing ESE papers. By no means do we claim to be fully objective, generalizable, or representative of the whole discipline. With that being said, writing papers in this way has worked pretty well for us so far. We hope that this guide can at least partially do the same for others.

Jake Zappin, Trevor Stalnaker, Oscar Chaparro et al.

This position paper examines the substantial divide between academia and industry within quantum software engineering. For example, while academic research related to debugging and testing predominantly focuses on a limited subset of primarily quantum-specific issues, industry practitioners face a broader range of practical concerns, including software integration, compatibility, and real-world implementation hurdles. This disconnect mainly arises due to academia's limited access to industry practices and the often confidential, competitive nature of quantum development in commercial settings. As a result, academic advancements often fail to translate into actionable tools and methodologies that meet industry needs. By analyzing discussions within quantum developer forums, we identify key gaps in focus and resource availability that hinder progress on both sides. We propose collaborative efforts aimed at developing practical tools, methodologies, and best practices to bridge this divide, enabling academia to address the application-driven needs of industry and fostering a more aligned, sustainable ecosystem for quantum software development.