Blockchain technology has been emerged in the last decade and has gained a lot of interests from several sectors such as finance, government, energy, health, etc. This paper gives a broad ranging survey of the application of blockchain in healthcare domain. In fact, the ongoing research in this area is evolving rapidly. Therefore, we have identified several use cases in the state of art applying the blockchain technology, for instance for sharing electronic medical records, for remote patient monitoring, for drug supply chain, etc. We have focused also on identifying limitations of studied approaches and finally we have discussed some open research issues and the areas of future research.

One important type of testing Web services compositions is load testing, as such applications solicit concurrent access by multiple users simultaneously. In this context, load testing of these applications seems an important task in order to detect problems under elevated loads. For this purpose, we propose a distributed and resource aware test architecture aiming to study the behavior of WS-BPEL compositions considering load conditions. The major contribution of this paper consists of (i) looking for the best node hosting the execution of each tester instance, then (ii) running a load test during which the composition under test is monitored and performance data are recorded and finally (iii) analyzing in a distributed manner the resulting test logs in order to identify problems under load. We also illustrate our approach by means of a case study in the healthcare domain considering the context of resource aware load testing.

This paper presents a model-based black-box testing approach for dynamically adaptive systems. Behavioral models of such systems are formally specified using timed automata. With the aim of obtaining the new test suite and avoiding its regeneration in a cost effective manner, we propose a selective test generation approach. The latter comprises essentially three modules: (1) a model differencing module that detects similarities and differences between the initial and the evolved behavioral models, (2) an old test classification module that identifies reusable and retestable tests from the old test suite, and finally (3) a test generation module that generates new tests covering new behaviors and adapts old tests that failed during animation. To show its efficiency, the proposed technique is illustrated through the Toast application and compared to the classical Regenerate All and Retest All approaches.