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This work has been developed with the financial support of the European Union's Horizon 2020 research and innovation programme under grant agreement No. 824231

Software is everywhere and the productivity of Software Engineers has increased radically with the advent of new specifications, design and programming paradigms and languages. The main objective of the DECODER project is to introduce radical solutions to increase productivity by increasing the abstraction level, at specification stage, using requirements engineering techniques to integrate more complete specifications into the development process, and formal methods to reduce the time and efforts for integration testing. DECODER project will develop a methodology and tools to improve the productivity of the software development process for medium-criticality applications in the domains of IoT, Cloud Computing, and Operating Systems by combining Natural Language Processing techniques, modelling techniques and Formal Methods. A radical improvement is expected from the management and transformation of informal data into material (herein called knowledge ) that can be assimilated by any party involved in a development process. The project expects an average benefit of 20% in terms of efforts on several use cases belonging to the beforehand mentioned domains and will provide recommendations on how to generalize the approach to other medium-critical domains. This work has been developed with the financial support of the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 824231 and the Spanish State Research Agency under the project TIN2017-84094-R and co-financed with ERDF.

Second (improved) version of D5.2

This paper aims to introduce the innovative work carried out in the Horizon 2020 DECODER project – acronym for “DEveloper COmpanion for Documented and annotatEd code Reference” – (Grant Agreement no. 824231) by linking the fields of natural language processing (NLP) and software engineering.  The project as a whole addresses the development of a framework, namely the Persistent Knowledge Monitor (PKM), that acts as a central infrastructure to store, access, and trace all the data, information and knowledge related to a given software or ecosystem. This meta-model defines the knowledge base that can be queried and analysed by all the tools integrated and developed in DECODER. Besides, the DECODER project offers a friendly user interface where each of the predefined three roles (i.e., developers, maintainers and reviewers) can access and query the PKM with their personal accounts.  The paper focuses on the NLP tools developed and integrated in the PKM, namely the deep learning models developed to perform variable misuse, code summarisation and semantic parsing. These were developed under a common work package – “Activities for the developer” – intended to precisely target developers, who can perform tasks such as detection of bugs, automatic generation of documentation for source code and generation of code snippets from natural languages instructions, among the multiple functionalities that DECODER offers. These tools assist and help the developers in the daily work, by increasing their productivity and avoiding loss of time in tedious tasks such as manual bug detection. Training and validation were conducted for four use cases in Java, C and C++ programming languages in order to evaluate the performance, suitability, usability, etc. of the developed tools.

Report on OpenCV quality assessment and code map 36This deliverable reports on the work carried out in T62

This deliverable provides the design of the proposed methodological framework.

Demonstrator of tools to build traceability matrix for Persistent Monitor, relying on D4.1.

Report on first version of techniques for code summarization [24]This report specifies the datasets used during T2.4 and also the techniques used to summarize code.

This report is an interim report of ongoing and planned activities within WP6.

This deliverable provides tool support based on Eclipse to the proposed methodological framework