Web APIs provide a systematic and extensible approach for application-toapplication interaction. Developers using web APIs are forced to accompany the API providers in their software evolution tasks. In order to understand the distress caused by this imposition on web API client developers we perform a semi-structured interview with six such developers. We also investigate how major web API providers organize their API evolution, and we explore how this affects source code changes of their clients. Our exploratory qualitative study of the Twitter, Google Maps, Facebook and Netflix web APIs analyzes the state of web API evolution practices and provides insight into the impact of service evolution on client software. In order to complement the picture and also understand how web API providers deal with evolution, we investigate the server-side and client-side evolution of two open-source web APIs, namely VirtualBox and XBMC. Our study is complemented with a set of observations regarding best practices for web API evolution.

Abstract-Web APIs provide a systematic and extensible approach for application-to-application interaction. Developers using web APIs are forced to accompany the API providers in their software evolution tasks. In order to understand the distress caused by this imposition on web API client developers we perform a semi-structured interview with six such developers. We also investigate how major web API providers organize their API evolution, and we explore how this affects source code changes of their clients. Our exploratory study of the Twitter, Google Maps, Facebook and Netflix web APIs analyzes the state of web API evolution practices and provides insight into the impact of service evolution on client software. Our study is complemented with a set of observations regarding best practices for web API evolution.

No abstract

Abstract-Test prioritization techniques select test cases that maximize the confidence on the correctness of the system when the resources for quality assurance (QA) are limited. In the event of a test failing, the fault at the root of the failure has to be localized, adding an extra debugging cost that has to be taken into account as well. However, test suites that are prioritized for failure detection can reduce the amount of useful information for fault localization. This deteriorates the quality of the diagnosis provided, making the subsequent debugging phase more expensive, and defeating the purpose of the test cost minimization.In this paper we introduce a new test case prioritization approach that maximizes the improvement of the diagnostic information per test. Our approach minimizes the loss of diagnostic quality in the prioritized test suite. When considering QA cost as the combination of testing cost and debugging cost, on the Siemens set, the results of our test case prioritization approach show up to a 53% reduction of the overall QA cost, compared with the next best technique .

SUMMARYIn this paper, we present two descriptive case studies covering the re-engineering and further evolution of adopting service oriented architecture (SOA) in industry. The first case was carried out for a company in the transport sector with an application portfolio of over 700 systems. The second case study was conducted for an organization in the public sector. The goal of both case studies is to identify possible benefits and drawbacks of realizing SOA in large organizations in order to obtain a better perspective on the real, rather than the assumed, benefits of SOA in practice. We describe how the two cases were developed and carried out, and discuss the experiences gained and lessons learned from adopting SOA in the two organizations. Based on these findings, we propose several directions for further research.key words: service oriented architecture. re-engineering. case study. system integration

Runtime testing is emerging as the solution for the integration and validation of software systems where traditional development-time integration testing cannot be performed, such as Systems of Systems or Service Oriented Architectures. However, performing tests during deployment or in-service time introduces interference problems, such as undesired side-effects in the state of the system or the outside world.This paper presents a qualitative model of runtime testability that complements Binder's classical testability model, and a generic measurement framework for quantitatively assessing the degree of runtime testability of a system based on the ratio of what can be tested at runtime vs. what would have been tested during development time. A measurement is devised for the concrete case of architecture-based test coverage, by using a graph model of the system's architecture. Concretely, two testability studies are performed for two component based systems, showing how to measure the runtime testability of a system.

No abstract

Diagnostic performance, measured in terms of the manual effort developers have to spend after faults are detected, is not the only important quality of a diagnosis. Efficiency, i.e., the number of tests and the rate of convergence to the final diagnosis is a very important quality of a diagnosis as well.In this paper we present an analytical model and a simulation model to predict the diagnostic efficiency of test suites when prioritized with the information gain algorithm. We show that, besides the size of the system itself, an optimal coverage density and uniform coverage distribution are needed to achieve an efficient diagnosis. Our models allow us to decide whether using IG with our current test suite will provide a good diagnostic efficiency, and enable us to define criteria for the generation or improvement of test suites.