How Does Web Service API Evolution Affect Clients?

Li, Jun; Xiong, Yingfei; Liu, Xuanzhe; Zhang, Lu

doi:10.1109/icws.2013.48

Cited by 67 publications

(47 citation statements)

References 15 publications

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“…[10] conducted an empirical study on recovery bugs in large-scale distributed systems. API changes problems was studied by [4,9,14,15]. Our work focuses on the bugs in the usage of deep learning libraries.…”

Section: Related Workmentioning

confidence: 99%

A comprehensive study on deep learning bug characteristics

Islam

Nguyen

Pan

et al. 2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

237

222

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Deep learning has gained substantial popularity in recent years. Developers mainly rely on libraries and tools to add deep learning capabilities to their software. What kinds of bugs are frequently found in such software? What are the root causes of such bugs? What impacts do such bugs have? Which stages of deep learning pipeline are more bug prone? Are there any antipatterns? Understanding such characteristics of bugs in deep learning software has the potential to foster the development of better deep learning platforms, debugging mechanisms, development practices, and encourage the development of analysis and verification frameworks. Therefore, we study 2716 high-quality posts from Stack Overflow and 500 bug fix commits from Github about five popular deep learning libraries Caffe, Keras, Tensorflow, Theano, and Torch to understand the types of bugs, root causes of bugs, impacts of bugs, bug-prone stage of deep learning pipeline as well as whether there are some common antipatterns found in this buggy software. The key findings of our study include: data bug and logic bug are the most severe bug types in deep learning software appearing more than 48% of the times, major root causes of these bugs are Incorrect Model Parameter (IPS) and Structural Inefficiency (SI) showing up more than 43% of the times. We have also found that the bugs in the usage of deep learning libraries have some common antipatterns that lead to a strong correlation of bug types among the libraries.

show abstract

Section: Related Workmentioning

confidence: 99%

A comprehensive study on deep learning bug characteristics

Islam

Nguyen

Pan

et al. 2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

237

222

View full text Add to dashboard Cite

show abstract

“…Some works have studied how web APIs evolve, characterizing change patterns and resulting challenges for application developers [16], and assessing how developers react to these changes [17]. Focusing on implications on applications, Espinha et al have shown that mobile applications show diverse behavior in light of web API evolution [6].…”

Section: Web Api Evolutionmentioning

confidence: 99%

Benchmarking Web API Quality

Bermbach

Wittern

2016

Lecture Notes in Computer Science

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Modern applications increasingly interact with web APIs -reusable components, deployed and operated outside the application, and accessed over the network. Their existence, arguably, spurs application innovations, making it easy to integrate data or functionalities. While previous work has analyzed the ecosystem of web APIs and their design, little is known about web API quality at runtime. This gap is critical, as qualities including availability, latency, or security can severely impact applications and user experience. In this paper, we revisit a 3-month, geo-distributed benchmark of popular web APIs, originally performed in 2015. We repeat this benchmark in 2018 and compare results from these two benchmarks regarding availability and latency. We furthermore introduce new results from assessing web API security measurements, collected both in 2015 and 2018, and results from our attempts to reach out to API providers with the results from our 2015 experiments. Our extensive experiments show that web API qualities vary 1.) based on the geo-distribution of clients, 2.) during our individual experiments, and 3.) between the two experiments. Our findings provide evidence to foster the discussion around web API quality, and can act as a basis for the creation of tools and approaches to mitigate quality issues.Index Terms-Web APIs, Benchmarking, Quality of Service ! 2. We denote an endpoint to be the combination of a resource, identified by a URL, and an HTTP method as proposed in [7].

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“…Li et al [11] present another empirical study on the evolution of web APIs how it affects the clients, but they focus more on the technical aspects of the problems. Their data and arguments are derived by examining the native client development toolkits on various programming languages provided by the service vendors.…”

Section: Related Workmentioning

confidence: 99%

Using WADL Specifications to Develop and Maintain REST Client Applications

Fokaefs

Stroulia

2015

2015 IEEE International Conference on Web Services

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Service orientation is one of the most popular paradigms for developing modular distributed software systems. In spite of the substantial research effort dedicated to the development of methods and tools to support SOAP-based serviceoriented application development, in practice, RESTful services have surpassed SOAP-based services in popularity and adoption, primarily due to the simplicity of their invocation. However, poor adoption of REST specification standards and lack of systematic development tools have given rise to many, more or less compliant, variants of the RESTful style constraints, which undermine the evolvability and interoperability of these systems. In this paper, we describe a tool that supports the systematization of RESTful application development, through the use of semiautomatically constructed WADL interface specifications, without compromising the ease of the overall practice. We illustrate the use and advantages of our tool on real-world REST APIs. Additionally, we comment on how REST APIs are documented, especially in comparison to the auto-generated WADLs. 1

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How Does Web Service API Evolution Affect Clients?

Cited by 67 publications

References 15 publications

A comprehensive study on deep learning bug characteristics

A comprehensive study on deep learning bug characteristics

Benchmarking Web API Quality

Using WADL Specifications to Develop and Maintain REST Client Applications

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