Context: With the current high trends of deploying and using web services in practice, effective techniques for maintaining high quality of Service are becoming critical for both service providers and subscribers/users. Service providers want to predict the quality of service during early stages of development before releasing them to customers. Service clients consider the quality of service when selecting the best one satisfying their preferences in terms of price/budget and quality between the services offering the same features. The majority of existing studies for the prediction of quality of service are based on clustering algorithms to classify a set of services based on their collected quality attributes. Then, the user can select the best service based on his expectations both in terms of quality and features. However, this assumption requires the deployment of the services before being able to make the prediction and it can be time-consuming to collect the required data of running web services during a period of time. Furthermore, the clustering is only based on well-known quality attributes related to the services performance after deployment. Objective: In this paper, we start from the hypothesis that the quality of the source code and interface design can be used as indicators to predict the quality of service attributes without the need to deploy or run the services by the subscribers. Method: We collected training data of 707 web services and we used machine learning to generate association rules that predict the quality of service based on the interface and code quality metrics, and antipatterns. Results: The empirical validation of our prediction techniques shows that the generated association rules have strong support and high confidence which confirms our hypothesis that source code and interface quality metrics/antipatterns are correlated with web service quality attributes which are response time, availability, throughput, successability, reliability, compliance, best practices, latency, and documentation. Conclusion: To the best of our knowledge, this paper represents the first study to validate the correlation between interface metrics, source code metrics, antipatterns and quality of service. Another contribution of our work consists of generating association rules between the code/interface metrics and quality of service that can be used for prediction purposes before deploying new releases.

内容：在当前部署和使用Web服务的当前高趋势下，保持高质量服务的有效技术对于服务提供商和订户/用户都变得至关重要。服务提供商希望在开发初期就预测服务质量，然后再将其发布给客户。服务客户在提供相同功能的服务之间在价格/预算和质量方面选择满足其偏好的最佳服务时，会考虑服务质量。现有的大多数预测服务质量的研究都是基于聚类算法，以基于收集的质量属性对一组服务进行分类。然后，用户可以在质量和功能方面根据自己的期望选择最佳服务。然而，这种假设需要先进行服务部署，然后才能进行预测，并且在一段时间内收集运行的Web服务所需的数据可能很耗时。此外，群集仅基于与部署后的服务性能相关的众所周知的质量属性。目的：在本文中，我们从以下假设开始：源代码和接口设计的质量可以用作指示指标来预测服务属性的质量，而无需由订户部署或运行​​服务。方法：我们收集了707个Web服务的训练数据，并使用机器学习来生成关联规则，这些规则基于接口和代码质量指标以及反模式来预测服务质量。结果：我们的预测技术的经验验证表明，所生成的关联规则具有强大的支持力和较高的置信度，这证实了我们的假设，即源代码和接口质量指标/反模式与Web服务质量属性相关联，这些属性是响应时间，可用性，吞吐量，可可靠性，合规性，最佳做法，延迟和文档。结论：据我们所知，本文代表了第一个验证接口指标，源代码指标，反模式和服务质量之间相关性的研究。我们工作的另一个贡献是在代码/接口指标和服务质量之间生成关联规则，这些规则可以在部署新版本之前用于预测。