Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
First public release of assistcurve, the software accompanying the manuscript Assistance curve analysis: evaluating the clinical value of AI-assisted decision making. What it does Assistance curve analysis extends decision curve analysis from prediction models to a clinician working with a model. The central quantity is the net benefit of assistance, ΔNB(p): the difference in net benefit between the decisions a clinician makes with an AI available and the decisions they make without it. Because net benefit is linear in the decisions, ΔNB depends only on the cases where the clinician changed their mind, and reduces to four numbers: ΔNB(p) = [ r_U(π_U − p) + r_L(p − π_L) ] / (1 − p) which gives a break-even rule anyone can apply: follow the AI upward only if more than p of the patients it talks you into treating really have the condition, and downward only if fewer than p of those it talks you out of treating do. The package provides the assistance curve, the switch decomposition, the break-even threshold, the number needed to consult, and an override profile that identifies which part of a model's output range earned its place on the screen. Contents assistcurve/ — the Python package R/assistcurve/ — the R package, with documentation and its own test suite analysis/ — the simulation study, the Collab-CXR analysis, the permutation test, and verify_reported_values.py data/ — derived analysis files figures/ — the six manuscript figures, light and dark Reproducibility 18 Python tests and 22 R tests over 88 expectations. Both suites check properties — the two theorems, the affine form of the curve, the additivity of the override bands — rather than stored output. analysis/verify_reported_values.py recomputes all 144 values reported in the manuscript and exits non-zero on any disagreement. CI runs both suites on Python 3.9/3.11/3.13 and R 3.6/release, verifies the published values, and asserts that the committed derived tables match a fresh run. The R and Python implementations agree to ~1e-16 on every deterministic quantity. Data The applied analysis uses Collab-CXR (Moehring et al., CC-BY 4.0), https://doi.org/10.17605/OSF.IO/Z7APQ. The 36 MB raw file is not redistributed here; data/ holds the derived files, which reproduce every design 2 and design 3 number and every real-data figure. See analysis/HOW_TO_RERUN.md. Licence MIT.
Agile - denoting "the quality of being agile, readiness for motion, nimbleness, activity, dexterity in motion" - software development methods are attempting to offer an answer to the eager business community asking for lighter weight along with faster and nimbler software development processes. This is especially the case with the rapidly growing and volatile Internet software industry as well as for the emerging mobile application environment. The new agile methods have evoked substantial amount of literature and debates. However, academic research on the subject is still scarce, as most of existing publications are written by practitioners or consultants. The aim of this publication is to begin filling this gap by systematically reviewing the existing literature on agile software development methodologies. This publication has three purposes. First, it proposes a definition and a classification of agile software development approaches. Second, it analyses ten software development methods that can be characterized as being "agile" against the defined criterion. Third, it compares these methods and highlights their similarities and differences. Based on this analysis, future research needs are identified and discussed.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 728 citations· ⚡54
Context: Software startups are newly created companies with no operating history and fast in producing cutting-edge technologies. These companies develop software under highly uncertain conditions, tackling fast-growing markets under severe lack of resources. Therefore, software startups present a unique combination of characteristics which pose several challenges to software development activities. Objective: This study aims to structure and analyze the literature on software development in startup companies, determining thereby the potential for technology transfer and identifying software development work practices reported by practitioners and researchers. Method: We conducted a systematic mapping study, developing a classification schema, ranking the selected primary studies according their rigor and relevance, and analyzing reported software development work practices in startups. Results: A total of 43 primary studies were identified and mapped, synthesizing the available evidence on software development in startups. Only 16 studies are entirely dedicated to software development in startups, of which 10 result in a weak contribution (advice and implications (6); lesson learned (3); tool (1)). Nineteen studies focus on managerial and organizational factors. Moreover, only 9 studies exhibit high scientific rigor and relevance. From the reviewed primary studies, 213 software engineering work practices were extracted, categorized and analyzed. Conclusion: This mapping study provides the first systematic exploration of the state-of-art on software startup research. The existing body of knowledge is limited to a few high quality studies. Furthermore, 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.· Information and Software Tec...· 394 citations· ⚡54
The growing literature on affect among software developers mostly reports on the linkage between happiness, software quality, and developer productivity. Understanding happiness and unhappiness in all its components -- positive and negative emotions and moods -- is an attractive and important endeavor. Scholars in industrial and organizational psychology have suggested that understanding happiness and unhappiness could lead to cost-effective ways of enhancing working conditions, job performance, and to limiting the occurrence of psychological disorders. Our comprehension of the consequences of (un)happiness among developers is still too shallow, being mainly expressed in terms of development productivity and software quality. In this paper, we study what happens when developers are happy and unhappy while developing software. Qualitative data analysis of responses given by 317 questionnaire participants identified 42 consequences of unhappiness and 32 of happiness. We found consequences of happiness and unhappiness that are beneficial and detrimental for developers' mental well-being, the software development process, and the produced artifacts. Our classification scheme, available as open data enables new happiness research opportunities of cause-effect type, and it can act as a guideline for practitioners for identifying damaging effects of unhappiness and for fostering happiness on the job.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· Journal of Systems and Softw...· 236 citations· ⚡13
Mobile phones have been closed environments until recent years. The change brought by open platform technologies such as the Symbian operating system and Java technologies has opened up a significant business opportunity for anyone to develop application software such as games for mobile terminals. However, developing mobile applications is currently a challenging task due to the specific demands and technical constraints of mobile development. Furthermore, at the moment very little is known about the suitability of the different development processes for mobile application development. Due to these issues, we have developed an agile development approach called Mobile-D. The Mobile-D approach is briefly outlined here and the experiences gained from four case studies are discussed.
P. Abrahamsson, Antti Hanhineva, H. Hulkko et al.· Conference on Object-Oriente...· 225 citations· ⚡18
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MIT News · Artificial Intelligence· news.mit.eduAug 17, 2026
A USAF cadet and a Lincoln Laboratory researcher found AI chatbots can help nontechnical service members produce viable software applications for their unique problems.
