Bug Localization in Model-Based Systems in the Wild

The companies that have adopted the Model-Driven Engineering (MDE) paradigm have the advantage of working at a high level of abstraction. Nevertheless, they have the disadvantage of the lack of tools available to perform bug localization at the model level. In addition, in an MDE context, a bug can be related to different MDE artefacts, such as design-time models, model transformations, or run-time models. Starting the bug localization in the wrong place or with the wrong tool can lead to a result that is unsatisfactory. We evaluate how to apply the existing model-based approaches in order to mitigate the effect of starting the localization in the wrong place. We also take into account that software engineers can refine the results at different stages. In our evaluation, we compare different combinations of the application of bug localization approaches and human refinement. The combination of our approaches plus manual refinement obtains the best results. We performed a statistical analysis to provide evidence of the significance of the results. The conclusions obtained from this evaluation are: humans have to be involved at the right time in the process (or results can even get worse), and artefact-independence can be achieved without worsening the results.

Glacier Runoff Variation Since 1981 in the Upper Naryn River Catchments, Central Tien Shan

Water resources in Central Asia strongly depend on glaciers, which in turn adjust their size in response to climate variations. We investigate glacier runoff in the period 1981–2019 in the upper Naryn basin, Kyrgyzstan. The basins contain more than 1,000 glaciers, which cover a total area of 776 km2. We model the mass balance and runoff contribution of all glaciers with a simplified energy balance melt model and distributed accumulation model driven by ERA5 LAND re-analysis data for the time period of 1981–2019. The results are evaluated against discharge records, satellite-derived snow cover, stake readings from individual glaciers, and geodetic mass balances. Modelled glacier volume decreased by approximately 6.7 km3 or 14%, and the majority of the mass loss took place from 1996 until 2019. The decreasing trend is the result of increasingly negative summer mass balances whereas winter mass balances show no substantial trend. Analysis of the discharge data suggests an increasing runoff for the past two decades, which is, however only partly reflected in an increase of glacier melt. Moreover, the strongest increase in discharge is observed in winter, suggesting either a prolonged melting period and/or increased groundwater discharge. The average runoff from the glacierized areas in summer months (June to August) constitutes approximately 23% of the total contributions to the basin’s runoff. The results highlight the strong regional variability in glacier-climate interactions in Central Asia.

Low-code development and model-driven engineering: Two sides of the same coin?

The last few years have witnessed a significant growth of so-called low-code development platforms (LCDPs) both in gaining traction on the market and attracting interest from academia. LCDPs are advertised as visual development platforms, typically running on the cloud, reducing the need for manual coding and also targeting non-professional programmers. Since LCDPs share many of the goals and features of model-driven engineering approaches, it is a common point of debate whether low-code is just a new buzzword for model-driven technologies, or whether the two terms refer to genuinely distinct approaches. To contribute to this discussion, in this expert-voice paper, we compare and contrast low-code and model-driven approaches, identifying their differences and commonalities, analysing their strong and weak points, and proposing directions for cross-pollination.

Development and Application of an Urban Flood Forecasting and Warning Process to Reduce Urban Flood Damage: A Case Study of Dorim River Basin, Seoul

Early and accurate flood forecasting and warning for urban flood risk areas is an essential factor to reduce flood damage. This paper presents the urban flood forecasting and warning process to reduce damage in the main flood risk area of South Korea. This process is developed based on the rainfall-runoff model and deep learning model. A model-driven method was devised to construct the accurate physical model with combined inland-river and flood control facilities, such as pump stations and underground storages. To calibrate the rainfall-runoff model, data of gauging stations and pump stations of an urban stream in August 2020 were used, and the model result was presented as an R2 value of 0.63~0.79. Accurate flood warning criteria of the urban stream were analyzed according to the various rainfall scenarios from the model-driven method. As flood forecasting and warning in the urban stream, deep learning models, vanilla ANN, Long Short-Term Memory (LSTM), Stack-LSTM, and Bidirectional LSTM were constructed. Deep learning models using 10-min hydrological time-series data from gauging stations were trained to warn of expected flood risks based on the water level in the urban stream. A forecasting and warning method that applied the bidirectional LSTM showed an R2 value of 0.9 for the water level forecast with 30 min lead time, indicating the possibility of effective flood forecasting and warning. This case study aims to contribute to the reduction of casualties and flood damage in urban streams and accurate flood warnings in typical urban flood risk areas of South Korea. The developed urban flood forecasting and warning process can be applied effectively as a non-structural measure to mitigate urban flood damage and can be extended considering watershed characteristics.

Design and Implementation of a Metadata Repository about UML Class Diagrams. A Software Tool Supporting the Automatic Feeding of the Repository

Model-Driven Engineering is largely recognized as the most powerful method for the design of complex software. This study deals with the automated archival of metadata about the content of UML class diagrams (a particularly relevant category of models) into a pre-existing repository. To define the structure of the repository, we started from the definition of a UML metamodel. From the latter, we derived the schema of the metadata repository. Then, a parser was developed that is responsible for extracting the useful information from the XMI file about class diagrams and enters it as metadata into the repository. The parser has been implemented as a Java web interface, while the metadata repository has been implemented as a PostgreSQL database based on the JSONB data type. The metadata repository is thought to support modelers in the initial phase of the process of the development of new models when looking for artifacts to start from. The schema of the metadata repository and the Java code of the parser are available from the authors.

A musculoskeletal model driven by muscle synergy-derived excitations for hand and wrist movements

Objective. Musculoskeletal model (MM) driven by electromyography (EMG) signals has been identified as a promising approach to predicting human motions in the control of prostheses and robots. However, muscle excitations in MMs are generally derived from the EMG signals of the targeted sensor covering the muscle, inconsistent with the fact that signals of a sensor are from multiple muscles considering signal crosstalk in actual situation. To identify more accurate muscle excitations for MM in the presence of crosstalk, we proposed a novel excitation-extracting method inspired by muscle synergy for simultaneously estimating hand and wrist movements. Approach. Muscle excitations were firstly extracted using a two-step muscle synergy-derived method. Specifically, we calculated subject-specific muscle weighting matrix and corresponding profiles according to contributions of different muscles for movements derived from synergistic motion relation. Then, the improved excitations were used to simultaneously estimate hand and wrist movements through musculoskeletal modeling. Moreover, the offline comparison among the proposed method, traditional MM and regression methods, and an online test of the proposed method were conducted. Main results. The offline experiments demonstrated that the proposed approach outperformed the EMG envelope-driven MM and three regression models with higher R and lower NRMSE. Furthermore, the comparison of excitations of two MMs validated the effectiveness of the proposed approach in extracting muscle excitations in the presence of crosstalk. The online test further indicated the superior performance of the proposed method than the MM driven by EMG envelopes. Significance. The proposed excitation-extracting method identified more accurate neural commands for MMs, providing a promising approach in rehabilitation and robot control to model the transformation from surface EMG to joint kinematics.

Applying Software Engineering Design Principles to Agile Architecture

Most enterprise IT systems are very complex with a combination of COBOL and Java programs running on multiple platforms. What is needed is a solid IT architecture that supports the operation and growth of a cross-platform IT system. It must enable the iterative and incremental development of applications that are foreseen in an agile development process. The design concept of such an architecture with its infrastructure and development tool is presented in this chapter. This design concept is based on the design principles and architectural patterns of software engineering. The architecture is a combination of layered, component-based, and service-oriented architectural patterns. The agile development process is based on a model-driven approach. The architecture and development approaches were first introduced in 2004. Since then, many applications have been developed on time and within budget.

Reuse in Agile Development Process

Reusability is a clear principle in software development. However, systematic reuse of software elements is not common in most organizations. Application programmers rarely design and create software elements for possible future reuse. In many agile software development processes, the project teams believe that the development of reusable software elements can slow down the project. This can be a misconception. This chapter examines various ways to reuse software. Three approaches to developing reusable software artifacts from 15 years of experience in the agile development process are presented. The first approach is to create generic programs or configurable frameworks that support similar solutions for a variety of use cases and environments. The reuse of patterns is the second approach presented. Another effective way is to use a model-driven approach with model patterns. These approaches help to speed deployment software. The final product is flexible and can easily be adapted to changes. This is one of the main goals of an agile approach.

Teaching Model-Driven Engineering in a Master's Program

Model-driven engineering (MDE) is an approach to software engineering that adopts models as the central artefact. Although the approach is promising in addressing major issues in software development, particularly in dealing with software complexity, and there are several success cases in the industry as well as growing interest in the research community, it seems that it has been hard to generalize its gains among software professionals. To address this issue, MDE must be taught at a higher-education level. This chapter presents a three-year experience in teaching MDE in a course of a master program in informatics engineering. The chapter provides details on how a project-based learning approach was adopted and evolved along three editions of the course. Results of a student survey are discussed and compared to those from another course. In addition, several other similar teaching experiences are analyzed.