Skip to content
#software testing Open access

Heritage response scoring: an image-free fixed-output reproduction core

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

This software and data package supports deterministic recomputation of response parsing and panel admission in a finite multimodal heritage panel, using frozen experimental responses. The package contains 9,600 follow-on raw-response records under aliases M1-M4; a 43,200-row derived evaluation table; the evaluator contract and implementation; executable analysis code; and 15 reference outputs. The reproduction command checks 38,400 follow-on evaluator instances against the derived table and regenerates all 15 reference outputs. The remaining 4,800 pilot-derived rows receive structural checks because their raw-response records are not included. M1-M4 identify frozen response groups, without disclosing exact model lineage or guaranteeing anonymity against inference from the outputs. Version 0.7.0 fixes analysis entry-point loading under Python safe-path settings and adds regression checks for running the tests and reproduction consecutively in the same extracted directory. The tests remove only their own generated bytecode file; the integrity checker continues to reject other undeclared files or caches. The calculation implementation, raw and derived data, protocols, analysis specifications, and all 15 reference outputs are unchanged from version 0.7.0-rc4. Execution has been checked locally with Python 3.12.14 using the standard library, including PYTHONSAFEPATH=1 and python -P. These checks establish deterministic recomputation, not independent scientific validation. The public core excludes source images and masks, weights and controlled identity mappings, individual expert identities or review returns, correspondence, and manuscript files. It does not provide fresh inference, establish expert validity or heritage accuracy, or reproduce every historical and supplementary analysis of the associated study. See README.md for execution instructions, DATA_DICTIONARY.md for the input structure, and RIGHTS_AND_EXCLUSIONS.md for scope and rights boundaries. The archive includes its file-integrity manifest and checksums. RELEASE_STATUS.json records the pre-upload build-time snapshot; the repository record provides the version-specific DOI and publication status after publication. Licensing is allocated by file type. All .py software files, including embedded comments and docstrings, are licensed under the MIT License. All non-.py documentation, data, selection and arrangement, and tables are licensed under Creative Commons Attribution 4.0 International (CC BY 4.0), limited to author-controlled copyright and similar rights and only to the extent the licensor is authorized. These are separate scopes, not a choice of either licence for every file. No ownership is asserted over uncopyrightable content. Third-party rights are not relicensed, and no rights to excluded images, weights or expert materials are granted. The notices do not certify downstream training permission or compliance with upstream third-party obligations. The complete MIT notice and further exclusions appear in RIGHTS_AND_EXCLUSIONS.md.

View source

Similar papers

#computer vision Review Sep 2017

Agile Software Development Methods: Review and Analysis

This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.

P. Abrahamsson, O. Salo, Jussi Ronkainen et al. · 727 citations · ⚡54
#computer vision Jun 2008

The impact of agile practices on communication in software development

The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.

M. Pikkarainen, Jukka Haikara, O. Salo et al. · 401 citations · ⚡48
#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54
#computer vision Review Mar 2008

Agile methods in European embedded software development organisations: a survey on the actual use and usefulness of Extreme Programming and Scrum

The results show that the embedded industry has been able to apply agile methods in its development processes and that the appreciation of the agile methods and their individual practices appears to increase once adopted and applied in practice.

O. Salo, P. Abrahamsson · 238 citations · ⚡9
#computer vision Open access Jul 2017

What happens when software developers are (un)happy

Consequences of happiness and unhappiness that are beneficial and detrimental for developers' mental well-being, the software development process, and the produced artifacts are found.

D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al. · 236 citations · ⚡13
#computer vision Open access Oct 2004

Mobile-D: an agile approach for mobile application development

The Mobile-D approach is briefly outlined here and the experiences gained from four case studies are discussed, which helped develop an agile development approach for mobile application development.

P. Abrahamsson, Antti Hanhineva, H. Hulkko et al. · 225 citations · ⚡18

Related blog posts

MIT News · Artificial Intelligence Oct 2, 2026

Documenting the tech worker movement

Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.

GPT-Lab Sep 23, 2026

Requirements Don’t Live in Isolation: What We’re Exploring with Req-Space

Requirements in large systems rarely exist in isolation. Their meaning depends on the wider project context - other requirements, policies, decisions, tests, and implementation details. That becomes especially important when AI is used for review, because spotting a possible conflict or gap is only the beginning. ReqSpace explores how AI, visualisation, and connected project context can help reviewers understand those findings, trace the relationships behind them, and focus on the questions that…

GPT-Lab Sep 17, 2026

Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering

AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.