The increasing number of languages used to engineer complex systems causes challenges to the development and maintenance processes of these languages. In this paper, we reflect on our experience in developing real life complex cyber-physical systems by using MDE techniques and DSLs. Firstly, we discuss a number of industrial challenges in the modeling software engineering domain. To address these challenges, we propose the concept of language architecture as an organizational principle for designing, reusing and maintaining DSLs and their infrastructure. Based on this, a metamodel for a DSL is designed and a tool support (LanArchi) is developed. Finally the possible future directions are given.

Scalable and comprehensive testing of mobile apps is extremely challenging. Every test input needs to be run with a variety of contexts, such as: device heterogeneity, wireless network speeds, locations, and unpredictable sensor inputs. The range of values for each context, e.g. location, can be very large. In this paper we present Caiipa, a cloud service for testing apps over an expanded mobile context space in a scalable way. It incorporates key techniques to make app testing more tractable, including a context test space prioritizer to quickly discover failure scenarios for each app. We have implemented Caiipa on a cluster of VMs and real devices that can each emulate various combinations of contexts for tablet and phone apps. We evaluate Caiipa by testing 265 commercially available mobile apps based on a comprehensive library of real-world conditions. Our results show that Caiipa leads to improvements of 11.1x and 8.4x in the number of crashes and performance bugs discovered compared to conventional UI-based automation (i.e., monkey-testing).

The design and tuning of networked virtual environments (NVEs), such as World of Warcraft (WoW), require understanding the in-NVE mobility characteristics of their citizens. Although many mobility-aware NVE systems already exist, their validation and further development have been hampered by the lack of public datasets and of comparison studies based on multiple datasets. To address these two issues, in this work we collect from WoW mobility traces for over 30,000 virtual citizens, and compare these traces with traces collected from Second Life (SL) where the environment is designed and changed significantly by the citizens themselves. Furthermore, motivated by the existence of numerous studies and models of networked real-world environments (NRE), we systematically compare the characteristics of two NVE and two NRE mobility traces. Our comparative study reveals that long-tail distributions characterize well various mobility characteristics, that the invisible boundary of human movement also appears for NVEs, and that area-visitation shows personal preferences. We also find several differences between NVE and NRE mobility characteristics.

Wi-Fi based localization has proven to be a compelling alternative to GPS for mobile devices. But Wi-Fi scanning consumes a large amount of energy on smartphones because they perform full scans, i.e. all the channels in their band(s) are visited. This inefficient behavior greatly reduces battery life, raising the threshold for user acceptance. We propose a novel, incremental approach that reduces the energy consumption of Wi-Fi localization by scanning just a few selected channels. We evaluate our incremental scanning approach on eight Android devices using traces from five test subjects. Our results show that, compared to full scans, incremental scanning can reduce the energy consumption between 20.64% and 57.79%. The modern smartphones included in our study all show an energy reduction of at least 40%.

Analyzing the power consumption of smartphones is difficult because of the complex interplay between soft- and hardware. Currently, researchers rely on mainly two options: external measurement tools, which are precise but constrain the mobility of the device and require the annotation of power traces; or modelling methods, which allow mobility and consider explicitly the state of events, but have less accuracy and lower sampling rates than external tools.

The programming and retasking of sensor nodes could benefit greatly from the use of a virtual machine (VM) since byte code is compact, can be loaded on demand, and interpreted on a heterogeneous set of devices. The challenge is to ensure good programming tools and a small footprint for the virtual machine to meet the memory constraints of typical WSN platforms. To this end we propose Darjeeling, a virtual machine modelled after the Java VM and capable of executing a substantial subset of the Java language, but designed specifically to run on 8- and 16-bit microcontrollers with 2--10 KB of RAM.

The Java programming language has potentially significant advantages for wireless sensor nodes but there is currently no feature-rich, open source virtual machine available. In this paper we present Darjeeling, a system comprising offline tools and a memory efficient run-time. The offline post-compiler tool analyzes, links and consolidates Java class files into loadable modules. The runtime implements a modified Java VM that supports multithreading and is designed specifically to operate in constrained execution environments such as wireless sensor network nodes and supports inheritance, threads, garbage collection, and loadable modules. We have demonstrated Java running on AVR128 and MSP430 microcontrollers at speeds of up to 70,000 JVM instructions per second.