How Much Are Machine Assistants Worth? Willingness to Pay for Machine Learning-Based Software Testing

Aug 5, 2026

Upload by Finn

Machine learning (ML) technologies drive many products and services, yet pricing remains uncertain. In software testing, ML-enabled tools can generate and support complex, human-like test case work, but little is known about users’ willingness to pay and which tool attributes matter most. A choice-based conjoint analysis with 119 participants in Germany examines desired features and payment preferences. Results indicate that high accuracy is most important, followed by ease of use and integration into existing environments.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Abstract

Introduction

ML services and pricing uncertainty

Role of ML in software testing

Costs, effort, and complexity in testing

Existing tools, market adoption, and provider incentives

Methodology

Choice-based conjoint analysis

Results

Accuracy, ease of use, and integration preferences

Implications for future research

Association for Information Systems AIS Electronic Library (AISeL) | ECIS 2023 Research Papers | ECIS 2023 Proceedings | | --- | --- | | 5-11-2023
How Much Are Machine Assistants Worth? Willingness to Pay for Machine Learning-Based Software Testing
Maren F. Mehler
Technical University of Darmstadt, [maren.mehler@tu-darmstadt.de](maren.mehler@tu-darmstadt.de)
Oliver A. Vetter
Technical University of Darmstadt, [oliver.vetter@tu-darmstadt.de](oliver.vetter@tu-darmstadt.de)
Follow this and additional works at: [https://aisel.aisnet.org/ecis2023_rp](https://aisel.aisnet.org/ecis2023_rp) | | Recommended Citation Mehler, Maren F. and Vetter, Oliver A., "How Much Are Machine Assistants Worth? Willingness to Pay for Machine Learning-Based Software Testing" (2023) . ECIS 2023 Research Papers. 355. [https://aisel.aisnet.org/ecis2023_rp/355](https://aisel.aisnet.org/ecis2023_rp/355) This material is brought to you by the ECIS 2023 Proceedings at AIS Electronic Library (AISeL) . It has been accepted for inclusion in ECIS 2023 Research Papers by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact](contact elibrary@aisnet.org)[ elibrary@aisnet.org](contact elibrary@aisnet.org). HOW MUCH ARE MACHINE ASSISTANTS WORTH? WILLINGNESS TO PAY FOR MACHINE LEARNING-BASED SOFTWARE TESTING Research Paper Maren F. Mehler, Technical University of Darmstadt, Germany, [mehler@is.tu-darmstadt.de](mehler@is.tu-darmstadt.de)[ ](mehler@is.tu-darmstadt.de)Oliver A. Vetter, Technical University of Darmstadt, Germany, [vetter@is.tu-darmstadt.de](vetter@is.tu-darmstadt.de) Abstract Machine Learning (ML) technologies have become the foundation of a plethora of products and services. While the economic potential of such ML-infused solutions has become irrefutable, there is still uncertainty onpricing. Currently, software testing is one area to benefit from ML services assisting in the creation of test cases; a task both complex and demanding human-like outputs. Yet, little is known on the willingness to pay of users, inhibiting the suppliers ’ incentive to develop suitable tools. To provide insights into desired features and willingness to pay for such ML-based tools, we perform a choicebased conjoint analysis with 119 participants in Germany. Our results show that a high level of accuracy is particularly important for users, followed by ease of use and integration into existing environments. Thus, we not only guide future developers on which attributes to prioritize but also which characteristics of ML-based services are relevant for future research. Keywords: Software Testing, Machine Learning, Choice-based Conjoint Analysis, Willingness to Pay. 1 Introduction An enormous amount of novel machine learning (ML) services has recently gained traction in the market. However, pricing these services is notoriously difficult and how much users are willing to pay is uncertain, especially since it is not clear which attributes are important to the users of ML services. Various use cases of ML services exist that support users in their day-to-day tasks and support independent decision making (Peters, Pumplun and Buxmann, 2020; Berente et al., 2021; von Wedeland Hagist, 2022) . A so far rarely explored use case of a ML-infused service supporting decision making, which shows high potential, is the application of ML in the context of software testing. Software testing is an essential part of the software development process as it ensures the quality of the software (Zhao, Hu and Gong, 2021) . Due to the high share of human labor in form of experience and knowledge, software testing consumes a lot of time and hence leads to high costs (Enoiu, Tukseferi and Feldt, 2020; Guveyi, Aktas and Kalipsiz, 2020) . According to a survey among CIOs, 23% of the annual IT expenditure is spent yearly on software quality assurance. Although the costs decreased slightly in recent years (cf. 35% in

FAQs

Which attributes do users prioritize when considering ML-based testing tools?

Users place the highest importance on accuracy, followed by ease of use and integration into existing environments.

Aug 5, 2026

Upload by Finn