A systematic mapping study of API usability evaluation methods

Rauf, Irum; Troubitsynå, Elena; Porres, Iván

doi:10.1016/j.cosrev.2019.05.001

Cited by 27 publications

(33 citation statements)

References 63 publications

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“…For example, researchers seek to enhance API operational reliability by analyzing automated test case generation [2] . Another example is API usability in support of the correct use of APIs to avoid cascading errors and potential system failure [30] . Boundary resources have also been analyzed as a means by which to share and extend design capabilities [25] .…”

Section: Boundary Resources: Artifact and Organizational Perspectivesmentioning

confidence: 99%

Roadblocks to Implementing Modern Digital Infrastructure: Exploratory Study of API Deployment in Large Organizations

Melville¹,

Kohli²

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Application programming interfaces (APIs) are an important component of digital infrastructure. Extensively employed in diverse industries, APIs are a boundary resource that enables new business models, enhances efficiency, and generates new sources of revenue. As little is known about how organizations deploy APIs, we conducted an exploratory examination of organizational deployment challenges of this important component of digital infrastructure. Analysis of semi-structured interview data collected within two large organizations reveals managerial challenges involving data, incentives, shared knowledge, and supplier management. Overall, our study contributes to knowledge about boundary resources while informing management practice concerning this emergent business imperative in the fourth industrial revolution.

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Section: Boundary Resources: Artifact and Organizational Perspectivesmentioning

confidence: 99%

Roadblocks to Implementing Modern Digital Infrastructure: Exploratory Study of API Deployment in Large Organizations

Melville¹,

Kohli²

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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show abstract

“…The evaluation of APIs usability has gained a lot of attention in the recent years [2], [14]. Based on the methods used, we can see two main categories: works that analyze the APIs design and their structural metrics, not taking into account how the API is being used (analytic methods [5], [9], [10]), and works that study how the API users are using the API (empirical methods [1], [13], [7]).…”

Section: Related Workmentioning

confidence: 99%

“…Actually, as Rauf et al [2] stated in their work on systematic mapping of API usability studies, the most used methods to evaluate APIs usability were empirical ones: usability tests, controlled experiments, surveys, etc. In fact, these methods are the most consolidated in software usability or human computer interaction (HCI) studies.…”

Section: Related Workmentioning

confidence: 99%

A Data-Driven Approach to Measure the Usability of Web APIs

Koçi

Franch

Jovanovic

et al. 2020

2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

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Application Programming Interfaces (APIs) are means of communication between applications, hence they can be seen as user interfaces, just with different kind of users, i.e., software or computers. However, the very first consumers of the APIs are humans, namely programmers. Based on the available documentation and the "ease of use" perception (sometimes led by corporate decisions and/or restrictions) they decide to use or not a specific API. In this paper, we propose a data-driven approach to measure web API usability, expressed through the predicted error rate. Following the reviewed state of the art in API usability, we identify a set of usability attributes, and for each of them we propose indicators that web API providers should refer to when developing usable web APIs. Our focus in this paper is on those indicators that can be quantified using the API logs, which indeed reflect the actual behaviour of programmers. Next, we define metrics for the aforementioned indicators, and exemplify them in our use case, applying them on the logs from the web API of District Health Information System (DHIS2) used at World Health Organization (WHO). Using these metrics as features, we build a classifier model to predict the error rate of API endpoints. Besides finding usability issues, we also drill down into the usage logs and investigate the potential causes of these errors.Index Terms-API usability, API logs, log mining, web API.

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“…All these data have enormous potential for data mining methods and subsequent analysis. We already have sufficient capabilities (hardware and software) to process, store, organize, and evaluate them, but unfortunately, this is not accessible to third parties [32,33,34].…”

Section: A Customer Data Collectionmentioning

confidence: 99%

Computer Estimation of Customer Similarity With Facebook Lookalikes: Advantages and Disadvantages of Hyper-Targeting

Semerádová

Weinlich

2019

IEEE Access

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The advertising systems and the algorithms they use are constantly evolving and expanding the possibilities for reaching potential customers. Hyper-targeting (also called microtargeting) is the use of detailed customer data and marketing automation to deliver highly targeted and personalized messages across a large number of channels. These campaigns are designed to appeal to specific people or small groups of customers. By using the ability to process large amounts of data through innovations, such as predictive analytics, marketers can gain a deeper understanding of their audiences, focusing on specific accounts and not on the entire segments. This reportedly allows B2B brands to target customers directly and provide unique personal and highly relevant experiences. However, the scientific evidence to support this claim is missing. Some previous studies even suggest a negative impact of highly personalized advertising content on user reactiveness and purchase behavior. In this article, we test the effects of different levels of personalized advertisements using the advanced campaign targeting tool called Facebook Lookalike Audiences. Facebook Lookalike Audiences works on the basis of the estimation of customer similarity based on the characteristics of a custom audience, as defined by the advertiser. We examine the performance of various targeting settings using data from 840 Facebook ads with different personalization levels. These advertisements are compared in terms of reach, number of reactions, frequency of impressions, number of clicks, average time spent on a website, number of viewed pages, number of conversions, and profitability. We believe that the findings presented in this article help clarify the factors that influence user reactiveness toward personalized online advertising using evidence from actual Facebook ad sets.

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A systematic mapping study of API usability evaluation methods

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Cited by 27 publications

References 63 publications

Roadblocks to Implementing Modern Digital Infrastructure: Exploratory Study of API Deployment in Large Organizations

Roadblocks to Implementing Modern Digital Infrastructure: Exploratory Study of API Deployment in Large Organizations

A Data-Driven Approach to Measure the Usability of Web APIs

Computer Estimation of Customer Similarity With Facebook Lookalikes: Advantages and Disadvantages of Hyper-Targeting

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