With JSON's increasing adoption, the need for structural constraints and validation capabilities led to JSON Schema, a dedicated meta-language to specify languages which are in turn used to validate JSON documents. Currently, the standardisation process of JSON Schema and the implementation of adequate tool support (e.g., validators and editors) are work in progress. However, the periodic issuing of newer JSON Schema drafts makes tool development challenging. Nevertheless, many JSON Schemas as language definitions exist, but JSON documents are still mostly edited in basic text-based editors. To tackle this challenge, we investigate in this paper how Model-Driven Engineering (MDE) methods for language engineering can help in this area. Instead of re-inventing the wheel of building up particular technologies directly for JSON, we study how the existing MDE infrastructures may be utilized for JSON. In particular, we present a bridge between the JSONware and Modelware technical spaces to exchange languages and documents. Based on this bridge, our approach supports language engineers, domain experts, and tool providers in editing, validating, and generating tool support with enhanced capabilities for JSON schemas and their documents. We evaluate our approach with Shipyard, a JSON Schema-based language for the workflow specification for Keptn, an open-source tool for DevOps automation of cloud-native applications. The results of the case study show that proper editors and language evolution support from MDE can be reused and, at the same time, the surface syntax of JSON is maintained.

Technical debt (TD) in a software system is a metaphor that tries to illustrate the remediation effort of the already introduced quality deficit and the impact thereof to the business value of the system. To address TD, various management activities are proposed, each addressing a particular purpose. Whereas the activities of debt identification and measurement are broadly considered in literature, the activities of debt prioritization and communication lack appropriate approaches with an economic perspective. This work proposes a TD prioritization approach. Therefore, it narrows down the focus of TD to design debt and relies on the quantification of design best practices. Further, the non-conformance of these practices is assessed by applying a benchmarking technique. As a result, the gained information is transferred into a portfolio-matrix to support the prioritization and communication of design remediation actions. The applicability and suitability of the approach are demonstrated by using the source code of the open source project GeoGebra.

The design debt metaphor tries to illustrate quality deficits in the design of a software and the impact thereof to the business value of the system. To pay off the debt, the literature offers various approaches for identifying and prioritizing these design flaws, but without proper support in aligning strategic improvement actions to the identified issues. This work addresses this challenge and examines the suitability of our proposed portfolio-based design assessment approach. Therefore, this investigation is conducted based on three case studies where the product source code was analyzed and assessed using our portfolio-based approach. As a result, the approach has proven to be able to recommend concrete and valuable design improvement actions that can be adapted to project constraints.

To measure object-oriented design quality, metric-based approaches have been established. These have then been enhanced by identifying design smells in code. While these approaches are useful for identifying hot spots that should be refactored, they are still too vague to sufficiently guide software developers to implement improvements. This is why our previous work focuses on measuring the compliance of source code with object-oriented design best practices. These design best practices were systematically derived from the literature and can be mapped to design principles, which can help reveal fundamental object-oriented design issues in a software product. Despite the successful applications of this approach in industrial and open source projects, there is little accepted knowledge about the importance of various design best practices. Consequently, this paper shows the result of an online survey aimed at identifying the importance of 49 design best practices on design quality. In total, 214 people participated in the survey, resulting in an average of 138 opinions for each practice. Based on these opinions, five very important, 21 important, 12 moderately important and 11 unimportant design best practices could be derived. This information about importance helps managing design improvements in a focused way.

Current software development trends have shortened release cycles and forced developers to implement short-term solutions that cannot cope with increasing product complexity. This phenomenon of introducing hasty design choices or applying bad design practices becomes something known as technical debt, in particular design debt. To pay off this debt, the literature offers approaches for identifying these design flaws; however, few methods for properly prioritizing investment efforts are available. In this paper, we propose an approach that supports the decision-making process regarding design improvements. It identifies violations of design best practices that are then arranged within a two-dimensional portfolio matrix. This matrix combines the importance of practices of design quality with actual achievement relative to a benchmark suite. To show the application of the approach in a quality-improvement process, we performed a feasibility study on three open-source projects and a benchmark suite containing 50 projects. This study clearly shows that the importance of the design best practices greatly impacts the improvement decisions and must be aligned with the strategic quality goals of the product.

The idea of automatizing the assessment of object-oriented design is not new. Different approaches define and apply their own quality models, which are composed of single metrics or combinations thereof, to operationalize software design. However, single metrics are too fine-grained to identify core design flaws and they cannot provide hints for making design improvements. In order to deal with these weaknesses of metric-based models, rules-based approaches have proven successful in the realm of source-code quality. Moreover, for developing a well-designed software system, design principles play a key role, as they define fundamental guidelines and help to avoid pitfalls. Therefore, this thesis will enhance and complete a rule-based quality reference model for operationalizing design principles and will provide a measuring tool that implements these rules. The validation of the design quality model and the measurement tool will be based on various industrial projects. Additionally, quantitative and qualitative surveys will be conducted in order to get validated results on the value of object-oriented design principles for software development.

MOTIVATION: For biological pathways, it is common to measure a gene expression time series after various knockdowns of genes that are putatively involved in the process of interest. These interventional time-resolved data are most suitable for the elucidation of dynamic causal relationships in signaling networks. Even with this kind of data it is still a major and largely unsolved challenge to infer the topology and interaction logic of the underlying regulatory network. RESULTS: In this work, we present a novel model-based approach involving Boolean networks to reconstruct small to medium-sized regulatory networks. In particular, we solve the problem of exact likelihood computation in Boolean networks with probabilistic exponential time delays. Simulations demonstrate the high accuracy of our approach. We apply our method to data of Ivanova et al. (2006), where RNA interference knockdown experiments were used to build a network of the key regulatory genes governing mouse stem cell maintenance and differentiation. In contrast to previous analyses of that data set, our method can identify feedback loops and provides new insights into the interplay of some master regulators in embryonic stem cell development. AVAILABILITY AND IMPLEMENTATION: The algorithm is implemented in the statistical language R. Code and documentation are available at Bioinformatics online. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary Materials are available at Bioinfomatics online.