Software engineering conferences bring together thousands of academicians and software practitioners so that academic research and professional practices can influence each other. In essence, a symbiotic relationship exists between the research community and the software industry, which must be maintained, nurtured and re-examined periodically. Given the major AI breakthroughs (e.g., LLMs) and large-scale adoption of AI by the software industry, a re-examination of the relationship between academia and the SE industry is highly warranted. In this position paper, we argue that the software engineering community is deeply concerned about its research impact and relevance to industry practices. By conducting an empirical study using the survey responses from the SE community, we not only provide compelling evidence supporting our position but also propose new calls for action and reforms in SE, and thus envision a new future for the software engineering community.
Artificial intelligence offers much promise, but its use in scientific research should be restrained so that the primary aim of academia -- advancing knowledge for humans -- is safeguarded.
Academia and industry each possess distinct advantages in advancing technological progress. Academia's core mission is to promote open dissemination of research results and drive disciplinary progress. The industry values knowledge appropriability and core competitiveness, yet actively engages in open practices like academic conferences and platform sharing, creating a knowledge strategy paradox. Highly novel and publicly accessible knowledge serves as the driving force behind technological advancement. However, it remains unclear whether industry or academia can produce more novel research outcomes. Some studies argue that academia tends to generate more novel ideas, while others suggest that industry researchers are more likely to drive breakthroughs. Previous studies have been limited by data sources and inconsistent measures of novelty. To address these gaps, this study conducts an analysis using four types of fine-grained knowledge entities (Method, Tool, Dataset, Metric), calculates semantic distances between entities within a unified semantic space to quantify novelty, and achieves comparability of novelty across different types of literature. Then, a regression model is con
The growing replication crisis across disciplines such as economics, finance, and other social sciences as well as computer science undermines the credibility of academic research. Current institutional solutions -- such as artifact evaluations and replication packages -- suffer from critical limitations, including shortages of qualified data editors, difficulties in handling proprietary datasets, inefficient processes, and reliance on voluntary labor. This paper proposes a novel framework leveraging new technological advances in trusted-execution environments (TEEs) -- exemplified by Intel Trust Domain Extensions (TDX) -- to address the replication crisis in a cost-effective and scalable manner. Under our approach, authors execute replication packages within a cloud-based TEE and submit cryptographic proofs of correct execution, for which journals or conferences can efficiently verify without re-running the code. This reallocates the operational burden to authors while preserving data confidentiality and eliminating reliance on scarce editorial resources. As a proof of concept, we validate the feasibility of this system through field experiments, reporting a pilot study replicatin
As systems grow more complex and incorporate AI, testing becomes more critical. Yet testing education in academia remains misaligned with both professional practice and the empirical nature of testing. Current curricula predominantly adopt a rationalist paradigm, emphasizing prescriptive methods and confirmation of expected outcomes. This limits students' ability to reason critically under uncertainty. In this position paper, we argue that testing should instead be taught as an empirical, inquiry-driven professional skill. We propose an instructional design based on the Four-Component Instructional Design (4C/ID) model to support whole-task learning. We introduce P4TEST, a pedagogical framework that makes explicit the core competencies, epistemic moves, and habits of mind involved in testing, while avoiding prescriptive processes. The paper outlines how P4TEST can guide curriculum design, scaffolding, and assessment in software testing education.
Industry-academia ML collaborations routinely fail to launch -- not for scientific reasons, but because academics must publish while companies must protect models trained on proprietary data, and no standard contract framework resolves this tension. Because contracts are negotiated by legal departments alone, many apparent legal disputes are incentive misalignment problems that only scientists at the table can correctly diagnose. We propose PBOS (Protect-the-Business / Open-Source-the-Science), a community-adoptable contract template anchored to a single technically-grounded boundary: pre-training artifacts (architectures, training code, benchmarks, untrained weights) are open science; post-training artifacts (weights trained on proprietary data) are business IP. This boundary is technically meaningful, legally clean, and auditable -- and could not have been drawn correctly without scientists at the negotiating table. We argue the ML community should adopt PBOS as its default contract for such collaborations.
The rapid advancement of artificial intelligence (AI) has demonstrated substantial potential in chemical engineering, yet existing AI systems remain limited in interdisciplinary collaboration and exploration of uncharted problems. To address these issues, we present the Cyber Academia-Chemical Engineering (CA-ChemE) system, a living digital town that enables self-directed research evolution and emergent scientific discovery through multi-agent collaboration. By integrating domain-specific knowledge bases, knowledge enhancement technologies, and collaboration agents, the system successfully constructs an intelligent ecosystem capable of deep professional reasoning and efficient interdisciplinary collaboration. Our findings demonstrate that knowledge base-enabled enhancement mechanisms improved dialogue quality scores by 10-15% on average across all seven expert agents, fundamentally ensuring technical judgments are grounded in verifiable scientific evidence. However, we observed a critical bottleneck in cross-domain collaboration efficiency, prompting the introduction of a Collaboration Agent (CA) equipped with ontology engineering capabilities. CA's intervention achieved 8.5% impro
In this short article, I would like to briefly summarize my research in the first 5 years in my university academia life in USA. I think that my research results obtained in these 5 years are the best in my career, at least which I like the most by myself. I wish that my experience in my junior academia career could be of some help to young researchers.
The increasing demand for domain-specific evaluation of large language models (LLMs) has led to the development of numerous benchmarks. These efforts often adhere to the principle of data scaling, relying on large corpora or extensive question-answer (QA) sets to ensure broad coverage. However, the impact of corpus and QA set design on the precision and recall of domain-specific LLM performance remains poorly understood. In this paper, we argue that data scaling is not always the optimal principle for domain-specific benchmark construction. Instead, we introduce Comp-Comp, an iterative benchmarking framework grounded in the principle of comprehensiveness and compactness. Comprehensiveness ensures semantic recall by covering the full breadth of the domain, while compactness improves precision by reducing redundancy and noise. To demonstrate the effectiveness of our approach, we present a case study conducted at a well-renowned university, resulting in the creation of PolyBench, a large-scale, high-quality academic benchmark. Although this study focuses on academia, the Comp-Comp framework is domain-agnostic and readily adaptable to a wide range of specialized fields. The source code
As Artificial Intelligence (AI) and Data Science (DS) become pervasive, addressing gender disparities and diversity gaps in their workforce is urgent. These rapidly evolving fields have been further impacted by the COVID-19 pandemic, which disproportionately affected women and minorities, exposing deep-seated inequalities. Both academia and industry shape these disciplines, making it essential to map disparities across sectors, occupations, and skill levels. The dominance of men in AI and DS reinforces gender biases in machine learning systems, creating a feedback loop of inequality. This imbalance is a matter of social and economic justice and an ethical challenge, demanding value-driven diversity. Root causes include unequal access to education, disparities in academic programs, limited government investments, and underrepresented communities' perceptions of elite opportunities. This chapter examines the participation of women and minorities in AI and DS, focusing on their representation in both industry and academia. Analyzing the existing dynamics seeks to uncover the collective and individual impacts on the lives of women and minority groups within these fields. Additionally,
Deductive verification is an effective method to ensure that a given system exposes the intended behavior. In spite of its proven usefulness and feasibility in selected projects, deductive verification is still not a mainstream technique. To pave the way to widespread use, we present a study investigating the factors enabling successful applications of deductive verification and the underlying issues preventing broader adoption. We conducted semi-structured interviews with 30 practitioners of verification from both industry and academia and systematically analyzed the collected data employing a thematic analysis approach. Beside empirically confirming familiar challenges, e.g., the high level of expertise needed for conducting formal proofs, our data reveal several underexplored obstacles, such as proof maintenance, insufficient control over automation, and usability concerns. We further use the results from our data analysis to extract enablers and barriers for deductive verification and formulate concrete recommendations for practitioners, tool builders, and researchers, including principles for usability, automation, and integration with existing workflows.
Modern software systems require various capabilities to meet architectural and operational demands, such as the ability to scale automatically and recover from sudden failures. Self-adaptive software systems have emerged as a critical focus in software design and operation due to their capacity to autonomously adapt to changing environments. However, educating students on this topic is scarce in academia, and a survey among practitioners identified that the lack of knowledgeable individuals has hindered its adoption in the industry. In this paper, we present our experience teaching a course on self-adaptive software systems that integrates theoretical knowledge and hands-on learning with industry-relevant technologies. To close the gap between academic education and industry practices, we incorporated guest lectures from experts and showcases featuring industry professionals as judges, improving technical and communication skills for our students. Feedback based on surveys from 21 students indicates significant improvements in their understanding of self-adaptive systems. The empirical analysis of the developed course demonstrates the effectiveness of the proposed course syllabus a
The rapid development of AI tools and implementation of LLMs within downstream tasks has been paralleled by a surge in research exploring how the outputs of such AI/LLM systems embed biases, a research topic which was already being extensively explored before the era of ChatGPT. Given the high volume of research around the biases within the outputs of AI systems and LLMs, it is imperative to conduct systematic literature reviews to document throughlines within such research. In this paper, we conduct such a review of research covering AI/LLM bias in four premier venues/organizations -- *ACL, FAccT, NeurIPS, and AAAI -- published over the past 10 years. Through a coverage of 189 papers, we uncover patterns of bias research and along what axes of human identity they commonly focus. The first emergent pattern within the corpus was that 82% (155/189) papers did not establish a working definition of "bias" for their purposes, opting instead to simply state that biases and stereotypes exist that can have harmful downstream effects while establishing only mathematical and technical definition of bias. 94 of these 155 papers have been published in the past 5 years, after Blodgett et al. (2
Despite progress toward gender parity, women remain underrepresented in academia, particularly in senior research positions. This study investigates the role of parenthood in shaping gender disparities in academic careers, focusing on the complex interplay between gender, childcare responsibilities, gender role beliefs, institutional support, and scientists' career achievements. Using a large-scale survey of 5,670 U.S. and Canadian academics, supplemented with bibliometric data from Web of Science, it reveals that childcare responsibilities significantly mediate gender disparities in both subjective and objective academic achievements, with women assuming a disproportionate share of childcare duties. In particular, women shoulder a greater caregiving load when their partners are employed full-time outside academia. However, egalitarian gender role beliefs have been playing an important role in shifting this structure by transforming women academics' behaviors. As women's egalitarian gender role beliefs strengthen, their childcare responsibilities tend to diminish-an effect not mirrored in men. Institutional parental support policies show mixed effects. While flexible work schedules
Lean R&D has been used at PUC-Rio to foster industry-academia collaboration in innovation projects across multiple sectors. This industrial experience paper describes recent experiences and evaluation results from applying Lean R&D in partnership with Petrobras in the oil and gas sector and Americanas in retail. The findings highlight Lean R&D's effectiveness in transforming ideas into meaningful business outcomes. Based on responses from 57 participants - including team members, managers, and sponsors - the assessment indicates that stakeholders find the structured phases of Lean R&D well-suited to innovation projects and endorse the approach. Although acknowledging that successful collaboration relies on various factors, this industrial experience positions Lean R&D as a promising framework for industry-academia projects focused on achieving rapid, impactful results for industry partners.
This study presents a bibliometric analysis of industry--academia collaboration in artificial intelligence (AI) research, focusing on papers from two major international conferences, AAAI and IJCAI, from 2010 to 2023. Most previous studies have relied on publishers and other databases to analyze bibliographic information. However, these databases have problems, such as missing articles and omitted metadata. Therefore, we adopted a novel approach to extract bibliographic information directly from the article PDFs: we examined 20,549 articles and identified the collaborative papers through a classification process of author affiliation. The analysis explores the temporal evolution of collaboration in AI, highlighting significant changes in collaboration patterns over the past decade. In particular, this study examines the role of key academic and industrial institutions in facilitating these collaborations, focusing on emerging global trends. Additionally, a content analysis using document classification was conducted to examine the type of first author in collaborative research articles and explore the potential differences between collaborative and noncollaborative research article
Effective data management and sharing are critical success factors in industry-academia collaboration. This paper explores the motivations and lessons learned from publishing open data sets in such collaborations. Through a survey of participants in a European research project that published 13 data sets, and an analysis of metadata from almost 281 thousand datasets in Zenodo, we collected qualitative and quantitative results on motivations, achievements, research questions, licences and file types. Through inductive reasoning and statistical analysis we found that planning the data collection is essential, and that only few datasets (2.4%) had accompanying scripts for improved reuse. We also found that authors are not well aware of the importance of licences or which licence to choose. Finally, we found that data with a synthetic origin, collected with simulations and potentially mixed with real measurements, can be very meaningful, as predicted by Gartner and illustrated by many datasets collected in our research project.
Large Language Models (LLMs) are transforming writing, reading, teaching, and knowledge retrieval in many academic fields. However, concerns regarding their misuse and erroneous outputs have led to varying degrees of trust in LLMs within academic communities. In response, various academic organizations have proposed and adopted policies regulating their usage. However, these policies are not based on substantial quantitative evidence because there is no data about use patterns and user opinion. Consequently, there is a pressing need to accurately quantify their usage, user trust in outputs, and concerns about key issues to prioritize in deployment. This study addresses these gaps through a quantitative user study of LLM usage and trust in academic research and education. Specifically, our study surveyed 125 individuals at a private R1 research university regarding their usage of LLMs, their trust in LLM outputs, and key issues to prioritize for robust usage in academia. Our findings reveal: (1) widespread adoption of LLMs, with 75% of respondents actively using them; (2) a significant positive correlation between trust and adoption, as well as between engagement and trust; and (3)
Previous researches on the Information retrieval (IR) field have focused on summarizing progress and synthesizing knowledge and techniques from individual studies and data-driven experiments, the extent of contributions and collaborations between researchers from different communities (e.g., academia and industry) in advancing IR knowledge remains unclear. To address this gap, this study explores several characteristics of information retrieval research in four areas: productivity patterns and preferred venues, the relationship between citations and downloads, changes in research topics, and changes in patterns of scientific collaboration, by analyzing 53,471 papers published between 2000 and 2018 from the Association for Computing Machinery (ACM) Digital Library dataset. Through the analysis and interpretation on empirical datasets, we find that academic research, industry research, and collaborative research between academia and industry focused on different topics. Among the collaboration models, Academia-Industry Collaboration is more oriented towards large teamwork. Collaborative networks between researchers in academia and industry suggest that the field of information retrie
[Context] Software Engineering (SE) education constantly seeks to bridge the gap between academic knowledge and industry demands, with active learning methods like Problem-Based Learning (PBL) gaining prominence. Despite these efforts, recent graduates struggle to align skills with industry needs. Recognizing the relevance of Industry-Academia Collaboration (IAC), Lean R&D has emerged as a successful agile-based research and development approach, emphasizing business and software development synergy. [Goal] This paper aims to extend Lean R&D with PBL principles, evaluating its application in an educational program designed by ExACTa PUC- Rio for Americanas S.A., a large Brazilian retail company. [Method] The educational program engaged 40 part-time students receiving lectures and mentoring while working on real problems, coordinators and mentors, and company stakeholders in industry projects. Empirical evaluation, through a case study approach, utilized structured questionnaires based on the Technology Acceptance Model (TAM). [Results] Stakeholders were satisfied with Lean R&D PBL for problem-solving. Students reported increased knowledge proficiency and perceived worki