A Systematic Review of API Evolution Literature

Recent software advances have led to an expansion of the development and usage of application programming interfaces (APIs). From millions of Android packages (APKs) available on Google Store to millions of open-source packages available in Maven, PyPI, and npm, APIs have become an integral part of software development. Like any software artifact, software APIs evolve and suffer from this evolution. Prior research has uncovered many challenges to the development, usage, and evolution of APIs. While some challenges have been studied and solved, many remain. These challenges are scattered in the literature, which hides advances and cloaks the remaining challenges. In this systematic literature review on APIs and API evolution, we uncover and describe publication trends and trending topics. We compile common research goals, evaluation methods, metrics, and subjects. We summarize the current state-of-the-art and outline known existing challenges as well as new challenges uncovered during this review. We conclude that the main remaining challenges related to APIs and API evolution are (1) automatically identifying and leveraging factors that drive API changes, (2) creating and using uniform benchmarks for research evaluation, and (3) understanding the impact of API evolution on API developers and users with respect to various programming languages.

Polyhedral-Based Compilation Framework for In-Memory Neural Network Accelerators

Memristor-based processing-in-memory architecture is a promising solution to the memory bottleneck in the neural network ( NN ) processing. A major challenge for the programmability of such architectures is the automatic compilation of high-level NN workloads, from various operators to the memristor-based hardware that may provide programming interfaces with different granularities. This article proposes a source-to-source compilation framework for such memristor-based NN accelerators, which can conduct automatic detection and mapping of multiple NN operators based on the flexible and rich representation capability of the polyhedral model. In contrast to previous studies, it implements support for pipeline generation to exploit the parallelism in the NN loads to leverage hardware resources for higher efficiency. The evaluation based on synthetic kernels and NN benchmarks demonstrates that the proposed framework can reliably detect and map the target operators. Case studies on typical memristor-based architectures also show its generality over various architectural designs. The evaluation further demonstrates that compared with existing polyhedral-based compilation frameworks that do not support the pipelined execution, the performance can upgrade by an order of magnitude with the pipelined execution, which emphasizes the necessity of our improvement.

E-Government 4.0: Managing APIs as Facilitators for Digital Transformation

Digital transformation is, among others, notably driven by servitization, and consequently, IT artifacts called Application Programming Interfaces (APIs), together with disruptive technologies and innovations. In order to set up proper policies, the understanding of the above factors is essential, including their influences on value adding chains. This paper therefore provides an analysis of APIs developments together with their impacts on digital transformation. As they can affect organizations and even national economies at strategic level, appropriate management from a systemic perspective is justified, including e-Government initiatives. Further, APIs cannot be treated without their relations to disruptive technologies, innovations and value chains. Therefore, the advancements of theoretical understanding of evolution of value chains, and practical directions for e-government initiatives are given in this paper. In addition, an archetype model is developed that provides a basis for further research efforts in the area. Received: 7 October 2021 / Accepted: 29 November 2021 / Published: 3 January 2022

What Happens When the Way to “Follow the Medium” Changes?

Digital methods allow social researchers and IT professionals to work together to produce instruments to comprehend current social phenomena. To develop these tools, they felt the need to “follow the medium” by reorganizing their data collection and analysis strategies on what they learned from the medium. For many years, digital research has been based on application programming interfaces (APIs) querying, an approach based on the extraction of records of data made available by the platforms through their programming interfaces. But what happens when the way to “follow the medium” changes? This contribution addresses the methodological challenges and the potential alternatives in research activities that affect the researchers' role due to recent restrictions. Two examples of research experience conducted before the APIs' closure are proposed in order to lead towards an initial reflection on its critical effects.

Performance comparison of programming interfaces on the example of REST API, GraphQL and gRPC

The article presents a comparison of the performance of three ways of implementing programming interfaces used in web applications - REST, GraphQL and gRPC. For the purposes of the research, three applications were developed, which were made in each of the indicated technologies and with the same functionalities. The applications were used for performance tests carried out with the use of the k6 tool. The applications are used to measure the execution time, performance and volume of processed data during display and adding operations. The obtained results allowed for the conclusion that the best interface in terms of performance (measured as the number of transactions per second) and server response time is REST. However, in terms of the smallest data volume, gRPC is the best choice.

TextMix: using NLP and APIs to generate chunked sentence scramble tasks

A Computer-Assisted Language Learning (CALL) application, TextMix, was developed as a proof-of-concept for applying Natural Language Processing (NLP) sentence chunking techniques to creating ‘sentence scramble’ learning tasks. TextMix addresses limitations of existing applications for creating sentence scrambles by using NLP to parse and scramble syntactic components of sentences, while connecting with Application Programming Interfaces (APIs) to provide repeated exposure to authentic sentences in the context of texts such as Wikipedia articles. In addition to identifying a novel application of NLP and APIs in CALL, this project highlights the need for teacher-friendly interfaces that prioritize pedagogically useful ways of chunking text.

An Experimental Performance Evaluation of Cloud-API-Based Applications

Cloud Application Programming Interfaces (APIs) have been developed to link several cloud computing applications together. API-based applications are widely used to provide flexible and reliable services over cloud platforms. Recently, a huge number of services have been attached to cloud platforms and widely utilized during a very short period of time. This is due to the COVID-19 lockdowns, which forced several businesses to switch to online services instantly. Several cloud platforms have failed to support adequate services, especially for extended and real-time-based applications. Early testing of the available platforms guarantees a level of suitability and reliability for the uploaded services. In this work, we first selected two different API-based applications from education and professional taxonomies, the two most recently used applications that have switched to the cloud environment. Then, we aimed to evaluate the performance of different API-based applications under different cloud platforms, in order to measure and validate the ability of these platforms to support these services. The advantages and drawbacks of each platform were experimentally investigated for each application.

Open science practices and risks arising from misuse of biological research

The proliferation of open science may inadvertently increase the chance of deliberate or accidental misuse of research. Here, we examine the interaction between open science practices and biosecurity and biosafety to identify risks and opportunities for risk mitigation. We argue that open data, code, and materials may increase risks from research with misuse potential, despite their general importance. For instance, increased access to protocols, datasets, and computational methods for viral engineering may increase the risk of release of enhanced pathogens. For this dangerous subset of research, both open science and biosecurity goals may be achieved by using access-controlled repositories or application programming interfaces. The increased use of preprints could challenge any strategy for risk mitigation that relies on assessment at the publication stage, emphasising the need for earlier oversight in the research lifecycle. Preregistration of research, a practice promoted by the open science community, provides an opportunity for achieving biosecurity risk assessment at the conception of research. Open science and biosecurity experts have an important role to play in enabling responsible research with maximal societal benefit.

The Proliferation of the Online Sales of Tourism Activities

The online sales of tourism activities are increasing despite once-sluggish growth, especially in contrast to other tourism segments, i.e., transportation and accommodation, which already had a more significant share of online sales. This article aims to provide a narrative as to why tourism activities took longer than other travel segments to be sold online and why the trend of being sold increasingly online will continue into the future. The article concludes that two key factors, the nature of tourism activities (i.e., subjective and customizable) and technological advancements, have contributed to this trend. These technological advancements include tourism activity booking software-as-a-service (SaaS), innovation in payments technology, application programming interfaces (APIs), big data, and dynamic packaging. Additionally, the COVID-19 pandemic further accelerated this trend.