On the Empirical Evidence of Microservice Logical Coupling. A Registered Report

[Context] Coupling is a widely discussed metric by software engineers while developing complex software systems, often referred to as a crucial factor and symptom of a poor or good design. Nevertheless, measuring the logical coupling among microservices and analyzing the interactions between services is non-trivial because it demands runtime information in the form of log files, which are not always accessible. [Objective and Method] In this work, we propose the design of a study aimed at empirically validating the Microservice Logical Coupling (MLC) metric presented in our previous study. In particular, we plan to empirically study Open Source Systems (OSS) built using a microservice architecture. [Results] The result of this work aims at corroborating the effectiveness and validity of the MLC metric. Thus, we will gather empirical evidence and develop a methodology to analyze and support the claims regarding the MLC metric. Furthermore, we establish its usefulness in evaluating and understanding the logical coupling among microservices

Machine Learning-Based Test Smell Detection

Context: Test smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous stud- ies have proved their harmfulness for test code maintainability and effectiveness. Therefore, researchers have been proposing automated, heuristic-based techniques to detect them. However, the performance of such detectors is still limited and dependent on thresholds to be tuned. Objective: We propose the design and experimentation of a novel test smell detection approach based on machine learning to detect four test smells. Method: We plan to develop the largest dataset of manually- validated test smells. This dataset will be leveraged to train six machine learners and assess their capabilities in within- and cross-project scenarios. Finally, we plan to compare our approach with state-of-the-art heuristic-based techniques

CATTO: Just-in-time Test Case Selection and Execution

Regression testing wants to prevent that errors, which have already been corrected once, creep back into a system that has been updated. A naïve approach consists of re-running the entire test suite (TS) against the changed version of the software under test (SUT). However, this might result in a time-and resource-consuming process; e.g., when dealing with large and/or complex SUTs and TSs. To avoid this problem, Test Case Selection (TCS) approaches can be used. This kind of approaches build a temporary TS comprising only those test cases (TCs) that are relevant to the changes made to the SUT, so avoiding executing unnecessary TCs. In this paper, we introduce CATTO (Commit Adaptive Tool for Test suite Optimization), a tool implementing a TCS strategy for SUTs written in Java as well as a wrapper to allow developers to use CATTO within IntelliJ IDEA and to execute CATTO just-in-time before committing changes to the repository. We conducted a preliminary evaluation of CATTO on seven open-source Java projects to evaluate the reduction of the test-suite size, the loss of fault-revealing TCs, and the loss of fault-detection capability. The results suggest that CATTO can be of help to developers when performing TCS. The video demo and the documentation of the tool is available at: https://catto-tool.github.io/acceptedVersionPeer reviewe

CoRoNNa: A Deep Sequential Framework to Predict Epidemic Spread

We propose CORONNA, a deep framework for epidemic prediction to analyse the spread of COVID-19 and, potentially, of other unknown viruses, based on a flexible integration of sequential and convolutional components. Importantly, our framework is general and can be specialised according to different analysis objectives. In this paper, the specific purpose is to optimise CORONNA for analysing the impact of different mobility containment policies on the epidemic. To this end, we integrate cumulative viral diffusion statistics and local demographic and mobility information of several countries. Our analysis confirms that mobility data have a strong, but delayed, effect on the viral spread. In this context, CORONNA has superior performances when compared with other frameworks that incorporate multivariate lagged predictors, and with simple LSTM models. On the contrary, no method is able to predict daily cases based only on lagged viral diffusion statistics

Latent and sequential prediction of the novel coronavirus epidemiological spread

In this paper we present CoRoNNa a deep sequential framework for epidemic prediction that leverages a flexible combination of sequential and convolutional components to analyse the transmission of COVID-19 and, perhaps, other undiscovered viruses. Importantly, our methodology is generic and may be tailored to specific analysis goals. We exploit CoRoNNa to analyse the impact of various mobility containment policies on the pandemic using cumulative viral dissemination statistics with local demographic and movement data from several nations. Our experiments show that data on mobility has a significant, but delayed, impact on viral propagation. When compared to alternative frameworks that combine multivariate lagged predictors and basic LSTM models, CoRoNNa outperforms them. On the contrary, no technique based solely on lagged viral dissemination statistics can forecast daily cases

A fully implantable device for intraperitoneal drug delivery refilled by ingestible capsules

Creating fully implantable robots that replace or restore physiological processes is a great challenge in medical robotics. Restoring blood glucose homeostasis in patients with type 1 diabetes is particularly interesting in this sense. Intraperitoneal insulin delivery could revolutionize type 1 diabetes treatment. At present, the intraperitoneal route is little used because it relies on accessing ports connecting intraperitoneal catheters to external reservoirs. Drug-loaded pills transported across the digestive system to refill an implantable reservoir in a minimally invasive fashion could open new possibilities in intraperitoneal delivery. Here, we describe PILLSID (PILl-refiLled implanted System for Intraperitoneal Delivery), a fully implantable robotic device refillable through ingestible magnetic pills carrying drugs. Once refilled, the device acts as a programmable microinfusion system for precise intraperitoneal delivery. The robotic device is grounded on a combination of magnetic switchable components, miniaturized mechatronic elements, a wireless powering system, and a control unit to implement the refilling and control the infusion processes. In this study, we describe the PILLSID prototyping. The device key blocks are validated as single components and within the integrated device at the preclinical level. We demonstrate that the refilling mechanism works efficiently in vivo and that the blood glucose level can be safely regulated in diabetic swine. The device weights 165 grams and is 78 millimeters by 63 millimeters by 35 millimeters, comparable with commercial implantable devices yet overcoming the urgent critical issues related to reservoir refilling and powering

Digital single-operator cholangioscopy in diagnostic and therapeutic bilio-pancreatic diseases: A prospective, multicenter study

Background and aim: Digital single-operator cholangioscopy (D-SOC) is an endoscopic procedure that is increasingly used for the management of bilio-pancreatic diseases. We aimed to investigate the efficacy and safety of D-SOC for diagnostic and therapeutic indications. Methods: This is a multicenter, prospective study(January 2016-June 2019) across eighteen tertiary centers. The primary outcome was procedural success of D-SOC. Secondary outcomes were: D-SOC visual assessment and diagnostic yield of SpyBite biopsy in cases of biliary strictures, stone clearance rate in cases of difficult biliary stones, rate of adverse events(AEs) for all indications. Results: D-SOC was performed in 369 patients (201(54,5%) diagnostic and 168(45,5%)therapeutic). Overall, procedural success rate was achieved in 360(97,6%) patients. The sensitivity, specificity, PPV, NPV and accuracy in biliary strictures were: 88,5%, 77,3%, 83,3%, 84,1% and 83,6% for D-SOC visual impression; 80,2%, 92,6%, 95,1%, 72,5% and 84,7% for the SpyBite biopsy, respectively. For difficult biliary stones, complete duct clearance was obtained in 92,1% patients (82,1% in a single session). Overall, AEs occurred in 37(10%) cases.The grade of AEs was mild or moderate for all cases, except one which was fatal. Conclusion: D-SOC is effective for diagnostic and therapeutic indications.Most of the AEs were minor and managed conservatively, even though a fatal event has happened that is not negligible and should be considered before using D-SOC

Metabolic control and complications in Italian people with diabetes treated with continuous subcutaneous insulin infusion

The objective of this cross-sectional study was to evaluate the degree of glycaemic control and the frequency of diabetic complications in Italian people with diabetes who were treated with continuous subcutaneous insulin infusion (CSII)