Tootfinder

Generative AI for Software Project Management: Insights from a Review of Software Practitioner Literature
Lakshana Iruni Assalaarachchi, Zainab Masood, Rashina Hoda, John Grundy
https://arxiv.org/abs/2510.10887

Generative AI for Software Project Management: Insights from a Review of Software Practitioner Literature
Software practitioners are discussing GenAI transformations in software project management openly and widely. To understand the state of affairs, we performed a grey literature review using 47 publicly available practitioner sources including blogs, articles, and industry reports. We found that software project managers primarily perceive GenAI as an "assistant", "copilot", or "friend" rather than as a "PM replacement", with support of GenAI in automating routine tasks, predictive analytics, co…

Polyglot Persistence in Microservices: Managing Data Diversity in Distributed Systems
Festim Halili, Anila Nuhiji, Diellza Mustafai Veliu
https://arxiv.org/abs/2509.08014 https:…

Polyglot Persistence in Microservices: Managing Data Diversity in Distributed Systems
Microservices architectures have become the foundation for developing scalable and modern software systems, but they also bring significant challenges in managing heterogeneous and distributed data. The pragmatic solution is polyglot persistence, the deliberate use of several different database technologies adapted to a given microservice requirement - is one such strategy. This paper examines polyglot persistence in microservice based systems. This paper brings together theoretical concepts wi…

Do you have the ability to write code for Apple platforms? Do you know what MDM / device management is? Do you understand what Compliance is? Do you care about being able to use a Mac or iPad or iPhone for work or school?
Does the idea of dealing with the bugs that can crop up from things like "this needs to be reliably managed like a server, but a human randomly sleeps it mid-process and when it wakes up it's in a different country!" sound interesting to you?
If so - come be my direct co-worker!
Need to be able to be based in/work within the US, but the team itself is fully remote. Multiple positions open.
Feel free to DM me with questions
https://jobs.apple.com/en-us/details/200628824-0836/software-engineer-test-infrastructure

Cognitive Agents Powered by Large Language Models for Agile Software Project Management
Konrad Cinkusz, Jaros{\l}aw A. Chudziak, Ewa Niewiadomska-Szynkiewicz
https://arxiv.org/abs/2508.16678

Cognitive Agents Powered by Large Language Models for Agile Software Project Management
This paper investigates the integration of cognitive agents powered by Large Language Models (LLMs) within the Scaled Agile Framework (SAFe) to reinforce software project management. By deploying virtual agents in simulated software environments, this study explores their potential to fulfill fundamental roles in IT project development, thereby optimizing project outcomes through intelligent automation. Particular emphasis is placed on the adaptability of these agents to Agile methodologies and…

InfraMind: A Novel Exploration-based GUI Agentic Framework for Mission-critical Industrial Management
Liangtao Lin, Zhaomeng Zhu, Tianwei Zhang, Yonggang Wen
https://arxiv.org/abs/2509.13704

InfraMind: A Novel Exploration-based GUI Agentic Framework for Mission-critical Industrial Management
Mission-critical industrial infrastructure, such as data centers, increasingly depends on complex management software. Its operations, however, pose significant challenges due to the escalating system complexity, multi-vendor integration, and a shortage of expert operators. While Robotic Process Automation (RPA) offers partial automation through handcrafted scripts, it suffers from limited flexibility and high maintenance costs. Recent advances in Large Language Model (LLM)-based graphical user…

The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Management
Tobias Lindenbauer, Igor Slinko, Ludwig Felder, Egor Bogomolov, Yaroslav Zharov
https://arxiv.org/abs/2508.21433

The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Management
Large Language Model (LLM)-based agents solve complex tasks through iterative reasoning, exploration, and tool-use, a process that can result in long, expensive context histories. While state-of-the-art Software Engineering ( SE) agents like OpenHands or Cursor use LLM-based summarization to tackle this issue, it is unclear whether the increased complexity offers tangible performance benefits compared to simply omitting older observations. We present a systematic comparison of these strategies …

A Systematic Review of FAIR-compliant Big Data Software Reference Architectures
Jo\~ao Pedro de Carvalho Castro, Maria J\'ulia Soares De Grandi, Cristina Dutra de Aguiar
https://arxiv.org/abs/2509.14370

A Systematic Review of FAIR-compliant Big Data Software Reference Architectures
To meet the standards of the Open Science movement, the FAIR Principles emphasize the importance of making scientific data Findable, Accessible, Interoperable, and Reusable. Yet, creating a repository that adheres to these principles presents significant challenges. Managing large volumes of diverse research data and metadata, often generated rapidly, requires a precise approach. This necessity has led to the development of Software Reference Architectures (SRAs) to guide the implementation pro…

BASFuzz: Towards Robustness Evaluation of LLM-based NLP Software via Automated Fuzz Testing
Mingxuan Xiao, Yan Xiao, Shunhui Ji, Jiahe Tu, Pengcheng Zhang
https://arxiv.org/abs/2509.17335

BASFuzz: Towards Robustness Evaluation of LLM-based NLP Software via Automated Fuzz Testing
Fuzzing has shown great success in evaluating the robustness of intelligent natural language processing (NLP) software. As large language model (LLM)-based NLP software is widely deployed in critical industries, existing methods still face two main challenges: 1 testing methods are insufficiently coupled with the behavioral patterns of LLM-based NLP software; 2 fuzzing capability for the testing scenario of natural language generation (NLG) generally degrades. To address these issues, we propos…

Establishing Technical Debt Management -- A Five-Step Workshop Approach and an Action Research Study
Marion Wiese, Kamila Serwa, Anastasia Besier, Ariane S. Marion-Jetten, Eva Bittner
https://arxiv.org/abs/2508.15570

Establishing Technical Debt Management -- A Five-Step Workshop Approach and an Action Research Study
Context. Technical debt (TD) items are constructs in a software system providing short-term benefits but hindering future changes. TD management (TDM) is frequently researched but rarely adopted in practice. Goal. This study aimed to establish a TDM process in an IT company based on a predefined workshop concept. We analyzed which research approaches practitioners adopted for each TD activity and the TDM's long-term effect on TD awareness. Method. We used action research (five action cycles in …

LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery
Arshia Akhavan, Alireza Hosseinpour, Abbas Heydarnoori, Mehdi Keshani
https://arxiv.org/abs/2508.12232

LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery
Issue-to-commit link recovery plays an important role in software traceability and improves project management. However, it remains a challenging task. A study on GitHub shows that only 42.2% of the issues are correctly linked to their commits. This highlights the potential for further development and research in this area. Existing studies have employed various AI/ML-based approaches, and with the recent development of large language models, researchers have leveraged LLMs to tackle this probl…