No abstract

Nowadays, going digital is a must for a company to thrive and remain competitive. The digital transformation allows companies to react timely and adequately to the constantly evolving markets. This transformation is not without challenges. Among these is the growing demand for skilled software developers. Low-code platforms have risen to mitigate this pressure point by allowing people with non-programming backgrounds to craft digital systems capable of solving business relevant problems.Professional development teams are composed of many different profiles -product owners, analysts, UX and UI designers, front-end and back-end developers, among others. Market competition puts unprecedented demands on the collaboration of these professionals. Current methodologies provide tools and approaches for many of these types of collaboration. However, the reality of established industry practices for UX and UI designers collaborating with frontend developers, still leaves a lot to improve in terms of effectiveness and efficiency.This work developed an innovative approach using model transformation and meta-modelling techniques that drastically improves the efficiency of transforming UX/UI design artefacts into low-code web-technology. The approach has been applied to a recognized and established enterprise-grade low-code platform and evaluated in practice by a team of professional designers and front-end developers. Preliminary practical results show savings between 20 and 75% according to the project complexity in the effort invested by development teams in the above mentioned process. CCS CONCEPTS• Software and its engineering → Application specific development environments.

Scheduling concurrent transactions to minimize contention is a well known technique in the Transactional Memory (TM) literature, which was largely investigated in the context of software TMs. However, the recent advent of Hardware Transactional Memory (HTM), and its inherently restricted nature, pose new technical challenges that prevent the adoption of existing schedulers: unlike software implementations of TM, existing HTMs provide no information on which data item or contending transaction caused abort.We propose Seer, a scheduler that addresses precisely this restriction of HTM by leveraging on an on-line probabilistic inference technique that identifies the most likely conflict relations, and establishes a dynamic locking scheme to serialize transactions in a fine-grained manner. Our evaluation shows that Seer improves the performance of the Intel TSX HTM by up to 2.5×, and by 62% on average, in TM benchmarks with 8 threads. These performance gains are not only a consequence of the reduced aborts, but also of the reduced activation of the HTM's pessimistic fall-back path.

This work aims to create an innovative system for analyzing and predicting the behaviour of object-oriented applications, with respect to the domain objects they manipulate, based on Markov Chains. The results are validated by the execution of the TPC-W and oo7 benchmarks. The oo7 benchmark has been modelled as a stochastic process through Monte Carlo simulations. The system is sufficiently flexible to be applied to a broad spectrum of object-oriented applications. The results are precise, regarding the observed behaviour, and the overheads introduced by the data acquisition are low.