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BALTICLSC: low-code SOFTWARE development PLATFORM FOR LARGE SCALE COMPUTATIONS

COMPUTING AND INFORMATICS 2021年第4期40卷 734-753页

作者： Marek, Krzysztof Smialek, Michal Rybinski, Kamil Roszczyk, Radoslaw Wdowiak, Marek Warsaw Univ Technol Plac Politech 1 PL-00661 Warsaw Poland

In modern times, innovation often requires performing complex computations in a short amount of time. However, for many small organisations and freelance innovators, large-scale computations remain beyond reach because of the small accessibility of computation resources and the lack of knowledge required to use them efficiently. The BalticLSC Platform is a software development and computing environment created to address this issue. This paper presents the associated software development process. The platform users can perform advanced computations using ready applications or develop new applications quickly from available components. This can be done using a visual notation called the Computation Application Language (CAL). CAL programs are developed in a dedicated online editor, through selecting and connecting reusable computation modules. If a required module is missing, it can be quickly created by encapsulating code inside a standardised container. The platform's ultimate goal is to relieve the developers from the need to understand the complexity of the distributed parallel computation environment. The platform was implemented in the form of an online software development portal. Validation of the platform consisted in the development of applications and modules by students not experienced in programming. The results of this validation acknowledge the required platform's characteristics.

关键词： Visual languages large-scale computing low-code development

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A search-and-fill strategy to code generation for complex software requirements

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INFORMATION AND SOFTWARE TECHNOLOGY 2025年 177卷

作者： Dong, Yukun Kong, Lingjie Zhang, Lulu Wang, Shuqi Liu, Xiaoshan Liu, Shuai Chen, Mingcheng China Univ Petr East China Qingdao Inst Software Coll Comp Sci & Technol Dongying Shandong Peoples R China

Context: The realm of software development has seen significant transformations with the rise of low-code development (LCD) and the integration of Artificial Intelligence (AI), particularly large language models, into coding practices. The proliferation of open-source software also offers vast resources for developers. Objective: We aim to combine the benefits of modifying retrieved code with the use of an extensive code repository to tackle the challenges of complex control structures and multifunctional requirements in software development. Method: Our study introduces a Search-and-Fill strategy that utilizes natural language processing (NLP) to dissect complex software requirements. It extracts control structures and identifies atomic function points. By leveraging large-scale pre-trained models, the strategy searches for these elements to fill in the automatically transformed program structures derived from descriptions of control structures. This process generates a code snippet that includes program control structures and the implementations of various function points, thereby facilitating both code reuse and efficient development. Results: We have validated the effectiveness of our strategy in generating code snippets. For natural language requirements involving multifunctional complex structures, we constructed two datasets: the Basic Complex Requirements Dataset (BCRD) and the Advanced Complex Requirements Dataset (ACRD). These datasets are based on natural language descriptions and Python code that were randomly extracted and combined. For the code snippets to be generated, we achieved the best results with the ACRD dataset, with BLEU-4 scores reaching up to 0.6326 and TEDS scores peaking at 0.7807. Conclusion: The Search-and-Fill strategy successfully generates a comprehensive code snippets, integrating essential control structures and functions to streamline the development process. Experimental results substantiate our strategy's efficacy in optimizing

关键词： low-code development Complex software requirements Search-and-Fill strategy code generation Natural language processing

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Fundamental Study of Empirical Experiments with New Institutional Research (IR) Practitioners Using Large Language Models (LLM) Such as ChatGPT 9th

Fundamental Study of Empirical Experiments with New Institut...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Seki, Masayuki Noda, Ikuhiro Omori, Msato Sakai, Tomoyuki Murakami, Katsuhiko Takamatsu, Kunihiko Kobe Tokiwa Univ Kobe Hyogo 6530838 Japan KEI Adv Tokyo 1020083 Japan Univ Tokyo Tokyo 1088639 Japan Tokyo Inst Technol Tokyo 1528550 Japan

ISBN: (纸本)9789819750344;9789819750351

This paper presents a fundamental study on the application of Large Language Models (LLM), such as ChatGPT, for enhancing Institutional Research (IR) practices among new practitioners. Emphasizing the transition to low or no-code methodologies, this study explored the potential of semi-automated analysis and visualization methods to streamline the data handling processes in higher education. With the advancement of LLMs, traditional programming barriers are being dismantled, allowing for more efficient and accessible data analysis. This study particularly investigated the feasibility of utilizing ChatGPT in reducing the programming workload for IR practitioners, which could lead to a substantial transformation in the field. Through empirical experiments and the development of new visualization techniques, the research determined the benefits and practicalities of integrating LLMs into IR, promoting a future where sophisticated data analysis becomes more inclusive and user-friendly for all IR professionals. The findings suggest that embracing such technological tools not only broadens the capabilities of IR practitioners but also strengthens the strategic impact of their role within educational institutions.

关键词： Institutional Research (IR) Large Language Model (LLM) ChatGPT Programming Data analysis Visualization Higher education Empirical experiment low-code development No-code development

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InstructPipe: Generating Visual Blocks Pipelines with Human Instructions and LLMs 25

InstructPipe: Generating Visual Blocks Pipelines with Human ...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Zhongyi Zhou Jing Jin Vrushank Phadnis Xiuxiu Yuan Jun Jiang Xun Qian Kristen Wright Mark Sherwood Jason Mayes Jingtao Zhou Yiyi Huang Zheng Xu Yinda Zhang Johnny Lee Alex Olwal David Kim Ram Iyengar Na Li Ruofei Du Google Tokyo Japan and The University of Tokyo Tokyo Japan Google Mountain View CA USA Google Sunnyvale CA USA Google Seattle WA USA Google Redmond WA USA Google Zurich Switzerland Google Palo Alto CA USA Google San Francisco CA USA

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Towards Augmented Enterprise Models as low-code Interfaces to Digital Systems 11th

Towards Augmented Enterprise Models as Low-Code Interfaces t...

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11th International Symposium on Business Modeling and Software Design (BMSD)

作者： Fill, Hans-Georg Haerer, Felix Muff, Fabian Curty, Simon Univ Fribourg CH-1700 Fribourg Switzerland

ISBN: (纸本)9783030799762;9783030799755

Traditionally, enterprise models have been used for representing knowledge on all aspects of an organization. This aided not only in composing a holistic picture of the different layers of an enterprise in terms of its business model, products and services, business processes and IT architecture, but also for describing the inter-dependencies between the layers. Depending on the degree of formalization, algorithms may be applied to the models, e.g. for simulations. With the upcoming of low-code approaches in software engineering, we regard in this position paper how similar concepts may be integrated in enterprise engineering. In particular we regard augmented enterprise models as interfaces to digital systems and illustrate this view with approaches for semantic technologies, data analytics and blockchain platforms. It is envisaged that such approaches will aid domain experts in integrating digital technologies in their daily work practices.

关键词： Enterprise modeling low-code development Digital systems

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Experiencing InstructPipe: Building Multi-modal AI Pipelines via Prompting LLMs and Visual Programming

Experiencing InstructPipe: Building Multi-modal AI Pipelines...

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CHI Conference on Human Factors in Computing Sytems (CHI)

作者： Zhou, Zhongyi Jin, Jing Phadnis, Vrushank Yuan, Xiuxiu Jiang, Jun Qian, Xun Zhou, Jingtao Huang, Yiyi Xu, Zheng Zhang, Yinda Wright, Kristen Mayes, Jason Sherwood, Mark Lee, Johnny Olwal, Alex Kim, David Iyengar, Ram Li, Na Du, Ruofei Univ Tokyo Tokyo Japan Google Res Mountain View CA USA Google Res Sunnyvale CA USA Google Res Redmond WA USA Google Res Zurich Switzerland Google Res San Francisco CA 94105 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9798400703317

Foundational multi-modal models have democratized AI access, yet the construction of complex, customizable machine learning pipelines by novice users remains a grand challenge. This paper demonstrates a visual programming system that allows novices to rapidly prototype multimodal AI pipelines. We first conducted a formative study with 58 contributors and collected 236 proposals of multimodal AI pipelines that served various practical needs. We then distilled our findings into a design matrix of primitive nodes for prototyping multimodal AI visual programming pipelines, and implemented a system with 65 nodes. To support users' rapid prototyping experience, we built InstructPipe, an AI assistant based on large language models (LLMs) that allows users to generate a pipeline by writing text-based instructions. We believe InstructPipe enhances novice users onboarding experience of visual programming and the controllability of LLMs by offering non-experts a platform to easily update the generation.

关键词： Visual Programming Large Language Models Visual Prototyping Node-graph Editor Graph Compiler low-code development Deep Neural Networks Deep Learning Visual Analytics

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Automated Refactoring of Unbounded Queries in Software Automation Platforms 24

Automated Refactoring of Unbounded Queries in Software Autom...

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24th International ACM/IEEE Conference on Model-Driven Engineering Languages and Systems (MODELS)

作者： Fernandes, Ines Pina Terra-Neves, Miguel Seco, Joao Costa NOVA Univ Lisbon Lisbon Portugal OutSystems Linda A Velha Portugal NOVA Univ Lisbon NOVA LINCS Lisbon Portugal

ISBN: (纸本)9781665424844

Software Automation Platforms (SAPs) enable faster development and reduce the need to use code to construct applications. SAPs provide abstraction and automation, resulting in a low-entry barrier for users with lower programming skills to become proficient developers. An unfortunate of using SAPs is to produce code with a higher technical-debt, since such developers are less familiar with the software development best practices. Hence, SAPs should aim to produce a simpler software construction and evolution pipeline beyond providing a rapid software development environment. An example of such high technical debt is the Unlimited Records anti-pattern, which occurs whenever queries are unbounded, i.e. the maximum number of records to be fetched is not explicitly limited. Limiting the number of records retrieved may, in many cases, improve the performance of applications by reducing screen loading time, thus making applications faster and more responsive, which is a top priority for developers. The OutSystems development environment signals the existence of unbounded queries as potential efficiency pitfall, as a development warning. There are scenarios where the limit was not defined because the query returns only one record by construction (e.g. get by primary key). The profusion of false positives in this analysis hinders the handling of true problems. Thus, we focus on the identification of such spurious warnings, and automatically fixing some straightforward occurrences, thus contributing to a more effective software evolution process. The total number of real-world findings reported in the OutSystems analysis tools, as of January 2021, consists of +1,3M unbounded queries. The state-of-the-art solution reports and recommends that developers solve these findings by hand. However, up to to this day, developers only solved 15% (approximate to 200K) of the reports. In this work, we propose an innovative approach for automated refactoring of the Unlimited Records anti-p

关键词： Refactoring Model-driven development low-code development Technical debt code analysis Anti-patterns

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How Effective Is Automated Trace Link Recovery in Model-Driven development? 28th

How Effective Is Automated Trace Link Recovery in Model-Driv...

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28th International Working Conference on Requirements Engineering: Foundation for Software Quality (REFSQ)

作者： Rasiman, Randell Dalpiaz, Fabiano Espana, Sergio Univ Utrecht Utrecht Netherlands

ISBN: (纸本)9783030984649;9783030984632

[Context and Motivation] Requirements Traceability (RT) aims to follow and describe the lifecycle of a requirement. RT is employed either because it is mandated, or because the product team perceives benefits. [Problem] RT practices such as the establishment and maintenance of trace links are generally carried out manually, thereby being prone to mistakes, vulnerable to changes, time-consuming, and difficult to maintain. Automated tracing tools have been proposed;yet, their adoption is low, often because of the limited evidence of their effectiveness. We focus on vertical traceability that links artifacts having different levels of abstraction. [Results] We design an automated tool for recovering traces between JIRA issues (user stories and bugs) and revisions in a modeldriven development (MDD) context. Based on existing literature that uses process and text-based data, we created 123 features to train a machine learning classifier. This classifier was validated via three MDD industry datasets. For a trace recommendation scenario, we obtained an average F-2-score of 69% with the best tested configuration. For an automated trace maintenance scenario, we obtained an F0.5-score of 76%. [Contribution] Our findings provide insights on the effectiveness of state-of-the-art trace link recovery techniques in an MDD context by using real-world data from a large company in the field of low-code development.

关键词： Requirement traceability Trace link recovery Model-driven development low-code development Machine learning

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Design Considerations for a Multi-user General-purpose Flow-based Visual Programming Environment 24

Design Considerations for a Multi-user General-purpose Flow-...

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17th International Conference on Advanced Visual Interfaces (AVI)

作者： Brandstatter, Ulrich Schenkenfelder, Bernhard Hohensinger, Doris Kirchtag, Harald Software Competence Ctr Hagenberg GmbH Hagenberg Austria KEBA Grp AG Linz Austria

ISBN: (纸本)9798400717642

A Visual Programming Environment (VPE) promises a shallow learning curve, improved code output performance, immediate visual feedback, rapid functional and graphical prototyping, and citizen development-all without handwritten code. With this work, we put forward novel design considerations for flow-based VPEs, addressing shortcomings of current VPEs discussed in literature. To a certain extent, these considerations are already being implemented in MVP, a Multi-user general-purpose flow-based Visual Programming environment that is currently under development.

关键词： Visual Programming low-code development Design Recommendations

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Democratizing Quality-Based Machine Learning development through Extended Feature Models 1

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26th International Conference on Fundamental Approaches to Software Engineering (FASE) Held as Part of the European Joint Conferences on Theory and Practice of Software (ETAPS)

作者： dAloisio, Giordano Di Marco, Antinisca Stilo, Giovanni Univ Aquila Laquila Italy

ISBN: (数字)9783031308260

ISBN: (纸本)9783031308253;9783031308260

ML systems have become an essential tool for experts of many domains, data scientists and researchers, allowing them to find answers to many complex business questions starting from raw datasets. Nevertheless, the development of ML systems able to satisfy the stake-holders' needs requires an appropriate amount of knowledge about the ML domain. Over the years, several solutions have been proposed to automate the development of ML systems. However, an approach taking into account the new quality concerns needed by ML systems (like fairness, interpretability, privacy, and others) is still missing. In this paper, we propose a new engineering approach for the quality-based development of ML systems by realizing a workflow formalized as a Software Product Line through Extended Feature Models to generate an ML System satisfying the required quality constraints. The proposed approach leverages an experimental environment that applies all the settings to enhance a given Quality Attribute, and selects the best one. The experimental environment is general and can be used for future quality methods' evaluations. Finally, we demonstrate the usefulness of our approach in the context of multi-class classification problem and fairness quality attribute.

关键词： Machine Learning System Software Quality Feature Models Software Product Line low-code development

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