Tootfinder

Build It Clean: Large-Scale Detection of Code Smells in Build Scripts
Mahzabin Tamanna, Yash Chandrani, Matthew Burrows, Brandon Wroblewski, Laurie Williams, Dominik Wermke
https://arxiv.org/abs/2506.17948

Build It Clean: Large-Scale Detection of Code Smells in Build Scripts
Build scripts are files that automate the process of compiling source code, managing dependencies, running tests, and packaging software into deployable artifacts. These scripts are ubiquitous in modern software development pipelines for streamlining testing and delivery. While developing build scripts, practitioners may inadvertently introduce code smells. Code smells are recurring patterns of poor coding practices that may lead to build failures or increase risk and technical debt. The goal o…

Advanced approach for Agile/Scrum Process: RetroAI
Maria Spichkova, Kevin Iwan, Madeleine Zwart, Hina Lee, Yuwon Yoon, Xiaohan Qin
https://arxiv.org/abs/2506.15172

Advanced approach for Agile/Scrum Process: RetroAI++
In Agile/Scrum software development, sprint planning and retrospective analysis are the key elements of project management. The aim of our work is to support software developers in these activities. In this paper, we present our prototype tool RetroAI++, based on emerging intelligent technologies. In our RetroAI++ prototype, we aim to automate and refine the practical application of Agile/Scrum processes within Sprint Planning and Retrospectives. Leveraging AI insights, our prototype aims to au…

Real-Time Agile Software Management for Edge and Fog Computing Based Smart City Infrastructure
Debasish Jana, Pinakpani Pal, Pawan Kumar
https://arxiv.org/abs/2506.12616

Real-Time Agile Software Management for Edge and Fog Computing Based Smart City Infrastructure
The evolution of smart cities demands scalable, secure, and energy-efficient architectures for real-time data processing. With the number of IoT devices expected to exceed 40 billion by 2030, traditional cloud-based systems are increasingly constrained by bandwidth, latency, and energy limitations. This paper leverages the ROOF (Real-time Onsite Operations Facilitation) framework with decentralized computing at intermediary fog and peripheral edge network layers to reduce latency by processing …

"The waste of having processes that create defects and then use inspection to catch them is certainly something to avoid. A significant part of the effort in code reviews should be geared toward capturing learning that can be applied to current processes to improve them so fewer bugs are created in the future.
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KHIFC-user friendly program for studying Heavy ion fusion barrier characteristics
H. C. Manjunatha, P. S. Damodara Gupta, N. Sowmya, K. N. Sridhar
https://arxiv.org/abs/2506.13823

KHIFC-user friendly program for studying Heavy ion fusion barrier characteristics
We have developed an application for studying heavy ion fusion barrier characteristics such as such as fusion barrier heights ($V_B$), positions ($R_B$) and curvature of the inverted parabola ($\hbarω$). We call this application as KHIFC (Kolar Heavy Ion fusion barrier Characteristics). This software application hosted in the domain "https://systematics-of-heavy-ion-fusion.vercel.app/". This KHIFC produces fusion cross-sections with the simple input of projectile, target and center of mass ene…

Comparing the Performance of MC/DC's on-GPU Event-based Processing Methods in Multigroup and Continuous-energy Problems
Braxton Cuneo, Joanna Piper Morgan, Ilham Variansyah, Kyle E. Niemeyer
https://arxiv.org/abs/2506.00263

Comparing the Performance of MC/DC's on-GPU Event-based Processing Methods in Multigroup and Continuous-energy Problems
Monte Carlo / Dynamic Code (MC/DC) is a portable Monte Carlo neutron transport package for rapid numerical methods exploration in heterogeneous and HPC contexts, developed under the auspices of the Center for Exascale Monte Carlo Neutron Transport (CEMeNT). To support execution on GPUs, MC/DC delegates resource and execution management to Harmonize (another CEMeNT software project). In this paper, we describe and compare the performance of the two methods that Harmonize currently provides: a st…

Is PMBOK Guide the Right Fit for AI? Re-evaluating Project Management in the Face of Artificial Intelligence Projects
Alexey Burdakov, Max Jaihyun Ahn
https://arxiv.org/abs/2506.02214

Is PMBOK Guide the Right Fit for AI? Re-evaluating Project Management in the Face of Artificial Intelligence Projects
This paper critically evaluates the applicability of the Project Management Body of Knowledge (PMBOK) Guide framework to Artificial Intelligence (AI) software projects, highlighting key limitations and proposing tailored adaptations. Unlike traditional projects, AI initiatives rely heavily on complex data, iterative experimentation, and specialized expertise while navigating significant ethical considerations. Our analysis identifies gaps in the PMBOK Guide, including its limited focus on data …

Automatic Multi-level Feature Tree Construction for Domain-Specific Reusable Artifacts Management
Dongming Jin, Zhi Jin, Nianyu Li, Kai Yang, Linyu Li, Suijing Guan
https://arxiv.org/abs/2506.03946

Automatic Multi-level Feature Tree Construction for Domain-Specific Reusable Artifacts Management
With the rapid growth of open-source ecosystems (e.g., Linux) and domain-specific software projects (e.g., aerospace), efficient management of reusable artifacts is becoming increasingly crucial for software reuse. The multi-level feature tree enables semantic management based on functionality and supports requirements-driven artifact selection. However, constructing such a tree heavily relies on domain expertise, which is time-consuming and labor-intensive. To address this issue, this paper pr…