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The OW2 open source community invites Open Source Experience Paris attendees to discover the H2020 DECODER platform in order to accelerate their software development, with quality and security. .

Paris, November 9, 2021 - The OW2 open source community booth #C16 is showcasing the DECODER platform, a new open source toolbox for DevSecOps teams at OSXP’21, 9-10 November, Paris Palais des Congrès. This platform is an outcome of the DECODER project that gathers seven members from four European countries, including one university (Universitat Politècnica de València), one research organization (CEA List), two industrial partners (Capgemini, Sysgo), two SMEs (Technikon, Tree Technology) and OW2 as non-profit organization. It is coordinated by Technikon, with CEA List as Technical Leader and involves OW2 as dissemination leader and use case provider.. 

Thanks to its methodology and its comprehensive toolset including the DECODER PKM (Persistent Knowledge Monitor), and specific modules designed for developers, testers and maintainers, the DECODER platform can automatically analyze and document IoT and cloud computing projects, providing a deeper understanding of software codes and their changes.

“The tools developed in DECODER have the potential to help developers manage the complexity of source code, as well as the complexity of artefacts such as requirements, diagrams, designs, documentation and test results,” says Armand Puccetti, CEA List Research Engineer, before adding: “Leveraging the information stored in this informal or semi-formal documents is one key goal of the DECODER project.”  

DECODER provides a comprehensive open source framework for the DevSecOps teams, leveraging technologies such as NLP (natural language processing), and state-of-the-art Artificial Intelligence and Machine Learning techniques. Its main objective is to offer more efficiency along the application lifecycle, through code understanding, readability, reusability, compliance and security. 

The DECODER project received funding from the European Union’s H2020 research and innovation programme under the grant agreement 824231.
For more information about DECODER, please visit: https://www.decoder-project.eu

About OW2
OW2 is an independent community dedicated to promoting open source software for information systems and to fostering their business ecosystems. OW2 federates 50+ organizations and 2500+ IT professionals world wide. OW2 hosts 50+ technology Projects, including: ASM, AuthzForce, BlueMind, CLIF, DocDoku, FusionDirectory, GLPI, JORAM, Knowage, LemonLDAP:NG, Lutece, OCS Inventory, Petals ESB, Prelude, ProActive, Rocket.Chat, SAT4J, SeedStack, Sympa, Telosys, Waarp, WebLab and XWiki. Visit https://www.ow2.org, follow us on Twitter @ow2.

• Download this Press Release (PDF format)
• Téléchargez le communique de presse français (au format PDF) 
• Check out OSXP'21 photos

Example of Slidedeck used at OSXP 2021 in Paris, 9-10 November 2021, to present DECODER and the ReachOut platform used for beta-testing campaigns.
A dedicated DECODER Pod on the OW2 booth also provided demos and up-to-date Press Releases available in English and French using QR code to avoid multiple printed documents. More photos of the OW2 booth at OSXP'21 in the event section.

Title: CodeMatcher, Searching Code Based on Sequential Semantics of Important Query Words
Authors: Chao Liu - Xin Xia - David Lo - Zhiwe Liu - Ahmed E. Hassan - Shanping Li

Abstract: To accelerate software development, developers frequently search and reuse existing code snippets from a large-scale codebase, e.g., GitHub. Over the years, researchers proposed many information retrieval (IR)-based models for code search, but they fail to connect the semantic gap between query and code. An early successful deep learning (DL)-based model DeepCS solved this issue by learning the relationship between pairs of code methods and corresponding natural language descriptions. 

Two major advantages of DeepCS are the capability of understanding irrelevant/noisy keywords and capturing sequential relationships between words in query and code. In this article, we proposed an IR-based model CodeMatcher that inherits the advantages of DeepCS (i.e., the capability of understanding the sequential semantics in important query words), while it can leverage the indexing technique in the IR-based model to accelerate the search response time substantially. 

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SWforumEU Project Spotlight is highlighting DECODER.

Developers have to drive through the complexity of software source code

The impact on business is to let Machine Learning help developers and maintainers to produce better code that is easier (and cheaper) to maintain as it is delivered with its related artefacts. After all, project is the result of the transformation of knowledge into code.

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Tree Technology, a partner in DECODER Project, posted an article about recent R&D efforts. 

Abstract: We have used Natural Language Processing (NLP) techniques in tools aimed to support and improve the software development and software quality processes for Java and C/C++ languages.

The use of complex models has increased performance in many common NLP tasks, such as named entity recognition, text classification, summarisation and translation among others. Besides, transfer learning has also become an interesting option when not much labelled data is available and knowledge learnt from one problem can be applied to a new but related task. In this context, our two NLP-based source code analysis tools - namely Variable Misuse and Code Summarisation - have been conceived by and for software developers.

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