Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Towards Symbolic Model-Based Mutation Testing: Combining Reachability and Refinement Checking

Model-based mutation testing uses altered test models to derive test cases that are able to reveal whether a modelled fault has been implemented. This requires conformance checking between the original and the mutated model. This paper presents an approach for symbolic conformance checking of action systems, which are well-suited to specify reactive systems. We also consider nondeterminism in our models. Hence, we do not check for equivalence, but for refinement. We encode the transition relation as well as the conformance relation as a constraint satisfaction problem and use a constraint solver in our reachability and refinement checking algorithms. Explicit conformance checking techniques often face state space explosion. First experimental evaluations show that our approach has potential to outperform explicit conformance checkers.Comment: In Proceedings MBT 2012, arXiv:1202.582

The Train Benchmark: cross-technology performance evaluation of continuous model queries

In model-driven development of safety-critical systems (like automotive, avionics or railways), well- formedness of models is repeatedly validated in order to detect design flaws as early as possible. In many indus- trial tools, validation rules are still often implemented by a large amount of imperative model traversal code which makes those rule implementations complicated and hard to maintain. Additionally, as models are rapidly increas- ing in size and complexity, efficient execution of validation rules is challenging for the currently available tools. Checking well-formedness constraints can be captured by declarative queries over graph models, while model update operations can be specified as model transformations. This paper presents a benchmark for systematically assessing the scalability of validating and revalidating well-formedness constraints over large graph models. The benchmark defines well-formedness validation scenarios in the railway domain: a metamodel, an instance model generator and a set of well- formedness constraints captured by queries, fault injection and repair operations (imitating the work of systems engi- neers by model transformations). The benchmark focuses on the performance of query evaluation, i.e. its execution time and memory consumption, with a particular empha- sis on reevaluation. We demonstrate that the benchmark can be adopted to various technologies and query engines, including modeling tools; relational, graph and semantic databases. The Train Benchmark is available as an open- source project with continuous builds from https://github. com/FTSRG/trainbenchmark

Assessing and Improving Industrial Software Processes

Software process is a complex phenomenon that involves a multitude of different artifacts, human actors with different roles, activities to be performed in order to produce a software product. Even though the research community is devoting a great effort in proposing solutions aimed at improving software process, several issues are still open. In this Thesis work I propose different solutions for assessing and improving software processes carried out in real industrial contexts. More in detail, I proposed a solution, based on ALM and MDE, for supporting Gap Analysis processes for assessing if a software process is carried out in accordance with Standards or Evaluation Framework. Then, I focused on a solution based on tool integration for the management of trace links among the artifacts involved in the software process. As another contribution, I proposed a Reverse engineering process and a tool, named EXACT, for supporting the analysis and comprehension of spreadsheet based artifacts involved in software development processes. Finally, I realized a semi-automatic approach, named AutoMative, for supporting the introduction in real Industrial software processes of SPL for managing the variability of the software products to be developed. Case studies conducted in real industrial settings showed the feasibility and the positive impact of the proposed solutions on real industrial software processes

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

A Bayesian learning approach to inconsistency identification in model-based systems engineering

Designing and developing complex engineering systems is a collaborative effort. In Model-Based Systems Engineering (MBSE), this collaboration is supported through the use of formal, computer-interpretable models, allowing stakeholders to address concerns using well-defined modeling languages. However, because concerns cannot be separated completely, implicit relationships and dependencies among the various models describing a system are unavoidable. Given that models are typically co-evolved and only weakly integrated, inconsistencies in the agglomeration of the information and knowledge encoded in the various models are frequently observed. The challenge is to identify such inconsistencies in an automated fashion. In this research, a probabilistic (Bayesian) approach to abductive reasoning about the existence of specific types of inconsistencies and, in the process, semantic overlaps (relationships and dependencies) in sets of heterogeneous models is presented. A prior belief about the manifestation of a particular type of inconsistency is updated with evidence, which is collected by extracting specific features from the models by means of pattern matching. Inference results are then utilized to improve future predictions by means of automated learning. The effectiveness and efficiency of the approach is evaluated through a theoretical complexity analysis of the underlying algorithms, and through application to a case study. Insights gained from the experiments conducted, as well as the results from a comparison to the state-of-the-art have demonstrated that the proposed method is a significant improvement over the status quo of inconsistency identification in MBSE.Ph.D