Maintenance of Automated Test Suites in Industry: An Empirical study on Visual GUI Testing

Context: Verification and validation (V&V) activities make up 20 to 50 percent of the total development costs of a software system in practice. Test automation is proposed to lower these V&V costs but available research only provides limited empirical data from industrial practice about the maintenance costs of automated tests and what factors affect these costs. In particular, these costs and factors are unknown for automated GUI-based testing. Objective: This paper addresses this lack of knowledge through analysis of the costs and factors associated with the maintenance of automated GUI-based tests in industrial practice. Method: An empirical study at two companies, Siemens and Saab, is reported where interviews about, and empirical work with, Visual GUI Testing is performed to acquire data about the technique's maintenance costs and feasibility. Results: 13 factors are observed that affect maintenance, e.g. tester knowledge/experience and test case complexity. Further, statistical analysis shows that developing new test scripts is costlier than maintenance but also that frequent maintenance is less costly than infrequent, big bang maintenance. In addition a cost model, based on previous work, is presented that estimates the time to positive return on investment (ROI) of test automation compared to manual testing. Conclusions: It is concluded that test automation can lower overall software development costs of a project whilst also having positive effects on software quality. However, maintenance costs can still be considerable and the less time a company currently spends on manual testing, the more time is required before positive, economic, ROI is reached after automation

Automated ANN alerts : one step ahead with mobile support

In this paper, I examine the potential of mobile alerting services empowering investors to react quickly to critical market events. Therefore, an analysis of short-term (intraday) price effects is performed. I find abnormal returns to company announcements which are completed within a timeframe of minutes. To make use of these findings, these price effects are predicted using pre-defined external metrics and different estimation methodologies. Compared to previous research, the results provide support that artificial neural networks and multiple linear regression are good estimation models for forecasting price effects also on an intraday basis. As most of the price effect magnitude and effect delay can be estimated correctly, it is demonstrated how a suitable mobile alerting service combining a low level of user-intrusiveness and timely information supply can be designed

Ocean Eddy Identification and Tracking using Neural Networks

Global climate change plays an essential role in our daily life. Mesoscale ocean eddies have a significant impact on global warming, since they affect the ocean dynamics, the energy as well as the mass transports of ocean circulation. From satellite altimetry we can derive high-resolution, global maps containing ocean signals with dominating coherent eddy structures. The aim of this study is the development and evaluation of a deep-learning based approach for the analysis of eddies. In detail, we develop an eddy identification and tracking framework with two different approaches that are mainly based on feature learning with convolutional neural networks. Furthermore, state-of-the-art image processing tools and object tracking methods are used to support the eddy tracking. In contrast to previous methods, our framework is able to learn a representation of the data in which eddies can be detected and tracked in more objective and robust way. We show the detection and tracking results on sea level anomalies (SLA) data from the area of Australia and the East Australia current, and compare our two eddy detection and tracking approaches to identify the most robust and objective method.Comment: accepted for International Geoscience and Remote Sensing Symposium 201

Analyzing the concept of technical debt in the context of agile software development: A systematic literature review

Technical debt (TD) is a metaphor that is used to communicate the consequences of poor software development practices to non-technical stakeholders. In recent years, it has gained significant attention in agile software development (ASD). The purpose of this study is to analyze and synthesize the state of the art of TD, and its causes, consequences, and management strategies in the context of ASD. Using a systematic literature review (SLR), 38 primary studies, out of 346 studies, were identified and analyzed. We found five research areas of interest related to the literature of TD in ASD. Among those areas, managing TD in ASD received the highest attention, followed by architecture in ASD and its relationship with TD. In addition, eight categories regarding the causes and five categories regarding the consequences of incurring TD in ASD were identified. Focus on quick delivery and architectural and design issues were the most popular causes of incurring TD in ASD. Reduced productivity, system degradation and increased maintenance cost were identified as significant consequences of incurring TD in ASD. Additionally, we found 12 strategies for managing TD in the context of ASD, out of which refactoring and enhancing the visibility of TD were the most significant. The results of this study provide a structured synthesis of TD and its management in the context of ASD as well as potential research areas for further investigation