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<p>The Haskell developer tool ecosystem, while greatly improving in recent years, has a performance problem. Haskell IDE Engine (HIE), the most popular implementation of the language server protocol for Haskell, is a great example of this. While it is an incredibly useful tool, its variable performance interferes with the productivity of Haskell developers.</p> <p>Profiling tools is another area where the Haskell ecosystem is lacking. While some tools do exist (for example ThreadScope), they are not as polished as similar tools for other languages, and do not allow for remote monitoring, which can be incredibly useful in distributed applications.</p> <p>I believe these two problems have a common solution: OpenTelemetry is a set of APIs and protocols made for instrumenting code, gathering traces and metrics, and analysing all that data, in a language-agnostic way, on a variety of independent clients.</p> <p>As my Google Summer of Code Project, I would like to first improve the newly-created haskell-opentelemetry library, use that to instrument HIE, and then optimise it based on my findings.</p>

<p>Mono is a open source development platform based on the <code>.Net Framework</code> and allows developers to build cross-platform applications. Mono's .NET implementation is based on the ECMA standards for <code>C#</code> and the <code>Common Language Infrastructure</code>. It includes both developer tools and the infrastructure needed to run .NET client and server applications on <code>Linux, Microsoft Windows, Mac OS X, BSD, Sun Solaris, Nintendo Wii, Sony PlayStation 3, Apple iPhone and Android</code>.</p> <p>The implementation of Pipe Streams in <code>Mono</code> doesn't support some functionalities such as parallel builds on msbuild. <code>CoreFX</code> is the foundational libraries for .NET Core. The implementation of Pipe Streams in CoreFx fixes the previous problems. So, the goal of the project was to import <code>System.IO.Pipes.PipeStream</code> from CoreFX to Mono. Doing this, new features are offered by Mono.</p>

Today, most users adopt Blockchain in order to collaborate with each other, however, it is difficult to manage a large blockchain network because of multiple organizations and nodes. Hyperledger Cello is aiming to resolve the following challenges, 1) Facilitate creation of blockchain network, can help user without blockchain background to setup their network nodes quickly. 2) Cross organization communication, can connect blockchain networks among multiple organizations. This project targets to design and implement a practical operational system equipping with decentralized functionalities to solve above challenges, based on Hyperledger Cello code base. Learning Objectives 1) Work closely with community experts and developers to learn the open-source culture and skills; 2) Learn the advanced knowledge inside the blockchain and distributed ledgers; 3) Practice hand-on experience with web application design and implement. Expected Outcome Help design and implement the blockchain operation dashboard and api server.

<p>The UI testing in LibreOffice is based on introspection code in c++ interacting with a testing framework in python through a simple UNO interface. To identify objects we use the ids that we introduced for loading dialogs from UI files. We still have unsupported items in LibreOffice UITesting Framework. Also We have a domain specific language that we used to log the events in its syntax. Also we have Logger that supports the UI elements of Visual Component Library and some application-specific events can also logged. So The project mainly goals is to Extend the ability of the existing UI testing framework to support the unsupported items that exist now. Then to Increase the ability of the DSL that used for testing by Be able to log more complex events and the events of the new added items that were unsupported before. Also Be able to generate more meaningful test cases with the DSL not just replaying the user action from the Logger. Finally to Add new generic methods for writing test cases for new bugs.</p>

<p>Open Event Orga Application is a client-side application for Open Event API designed for Event Organizers. The app provides functionality to Create Events, Create Tickets, Check In Attendee, Scan QR Code, Sales Overview, User Authentication etc.</p> <p>To further increase app's performance, user experience and expand its horizon, the following enhancements are proposed -</p> <p>● Migration to MVVM Architecture.</p> <p>● Background Job Scheduling for Offline Support.</p> <p>● Implementing various core modules comprising Orders, Sessions, Call for Speakers etc</p> <p>● Analytics and check-in/check-out functionalities.</p> <p>This will be accompanied by adding the required REST API and resolving bugs in Open Event Server project.</p> <p>Best available and tested technologies will be used to implement the above enhancements and test them thoroughly to achieve high-performance.</p>

<p>ROOT is the data processing framework created at CERN - at the heart of the research on high-energy physics. Every day, thousands of physicists use ROOT applications to analyze their data or to perform simulations. The ROOT software framework is foundational for the HEP ecosystem, providing capabilities such as IO, a C++ interpreter, GUI, and math libraries. It uses object-oriented concepts and build-time modules to layer between components. This project aims to provide additional functionality using a package manager over the minimal base install of core features. It involves defining ROOT modules, packages and package manager, mainly to scale the large codebase of the project. The current development involves creating a modular version of ROOT that provides a minimal base install of core features, then later adding functionality using the package manager. This requires introducing new layering mechanisms and extending the functionality of the existing ROOT package manager prototype.</p>

This project aims to increase BLAS (Basic Linear Algebra Subprograms) support in stdlib via the development of JavaScript, C, Fortran, and WebAssembly implementations capable of execution in both client- and server-side web applications. BLAS routines are divided into 3 levels: Level 1 (vector-vector operations), Level 2 (vector-matrix operations), Level 3 (matrix-matrix operations). While stdlib currently supports most Level 1 operations, several Level 2 and 3 routines involving specialized matrix storage types are currently missing. I aim to address this gap, with a particular emphasis on those routines which are key dependencies of LAPACK routines, such as dgetc2, dgbtf2, and others. A key outcome of this project will be a set of performance-optimized APIs for lower-level linear algebra operations which will subsequently help facilitate the development of high-performance machine learning and statistical analysis libraries and applications supporting execution on the web.

Apache Fineract’s current command processing system relies on a centralized service and untyped JSON payloads, which limits type safety and causes errors to surface only at runtime. In a financial system, such issues can disrupt critical operations and reduce reliability. This project proposes to migrate selected modules to the new CQRS-based fineract-command architecture, which introduces typed commands, structured handler dispatch, and extensible processing hooks. The migration will cover the full request lifecycle—from REST APIs to business logic—while strictly preserving backward compatibility through unchanged integration tests. Key deliverables include migrating open and unassigned modules to the new architecture, introducing type-safe DTOs with validation, replacing legacy JAX-RS endpoints with Spring Web MVC controllers, integrating the CommandPipeline with typed handlers, removing obsolete legacy components, and producing a comprehensive migration guide to enable future contributions.

<p>Create a Kiwix hotspot application that would be an extension to the kiwix android application. It would allow the users to share their ZIM Files with others over the same network. This can be made possible by:</p> <ul> <li>Creating a layout for the user to select the files he wishes to host.</li> <li>Working with a web server i.e. kiwix-serve. We can communicate with it via JNI(Java Native Interface).</li> <li>Working with WIFI hotspot(without using data).</li> <li>Presenting the IP address where the files have been hosted.</li> <li>Some networking tricks to polish the application.</li> </ul> <p>Other devices can access the ZIM files by just connecting to the hotspot and simply entering the ip address into their browser.</p> <p>This project would be really beneficial in areas especially where internet is not so easily available/accessible. For example, a teacher who has the kiwix android application would be able to share the ZIM files with all the students offline.</p>

<p>Radiation is the transmission of energy in the form of waves or sub-atomic particles.In space missions the major concern is the particle radiation.Energetic particle radiation passing through human body could damage the cells or DNA causing an increased risk for cancer. Especially when out of Earth's magnetic field protection, astronaut's are exposed to ionizing radiation with doses in the range from 50 to 2,000 mSv(milli Sievert). The evidence of cancer risk from ionizing radiation is extensive for radiation doses that are above about 50 mSv. Hence it is important to forecast the radiation events and train crew members to tackle the issue of radiation effectively in simulated environments(Mars city project).</p> <p>The aim of the project is to build a reliable system(Tango server) to :</p> <p>1) Alert crew about the incoming SEP event if the radiation level is above the SWPC set threshold.</p> <p>2) Issue the “all - clear” signal once the event has passed Mars.</p>

<p>OONF (OLSRv2) is a link state routing protocol. It works sending periodical messages to his neighbors with the aim of transmitting information about topology changes. With these information each node of the network is able calculate the paths to reach any other destination. These messages are periodically generated, based on the configuration parameter that regulates the sending interval. A short period will make the network react rapidly but it will also cause a large overhead due to control messages. Pop Routing is a recent technique that takes advantage of the knowledge of the network topology to find the optimal value for the OONF’s timers. Using Pop Routing every node computes its own “betweenness centrality” and uses it to calculate the optimal trade-off between convergence and overhead. The algorithm has been developed at the UniTN and the needed libraries to compute the BC are available as free software. My goal is to build a daemon that can calculate autonomously the BC of the network, together with a plugin for OONF that will use this value to tune its timers.</p>

<p>The relevance of ARM processors is rising. Especially since ARM recently started targeting the servers and desktop market, thus going beyond the mobile devices. That’s why malware targeting ARM is becoming an even bigger threat every day. On the other hand, it is still the x86 architecture for which most reverse engineering tools are developed. This Google Summer of Code (GSoC) project aims to add ARM support for both the dynamic analysis framework DRAKVUF and the virtual machine introspection (VMI) library libvmi. The idea is to base our project upon the result of an earlier GSoC project which added altp2m support for Xen on ARM. The altp2m subsystem defines different views on the guest’s physical memory and allows to dynamically switch among them. With altp2m and DRAKVUF we are able to build a stealthy VMI framework. In summary, the goal of the GSoC project is to add support for DRAKVUF for ARM and thus provide more options for reverse engineering on the ARM architecture.</p>

<p>SNARE is a web application honeypot sensor attracting all sort of maliciousness from the Internet. The web page is generated by cloning a real web application and injecting known vulnerabilities. SNARE connects to TANNER, a remote data analysis, and classification service, to evaluate HTTP requests and composing the response then served by SNARE.</p> <p>Aim: Spend the summer improving SNARE/TANNER.</p> <p>Project Goals:</p> <ol> <li>Reconstructing SNARE and CLONER code-base with syntax changing.</li> <li>Improve storing and analysing sessions in TANNER.</li> <li>Improve TANNER-web UI functionalities.</li> <li>Implement Tanner api authorization.</li> <li>Investigate various types of server fingerprinting and evaluate how SNARE performs.</li> <li>Move SNARE/TANNER to landscape.io.</li> <li>Dependency management using pipenv. (backup for goal #4)</li> </ol>

<p>The proposed project is concerned with the implementation <em>Automatic Generation of Qualia Relations between Lexical Units in FrameNet</em>. The main point of the project is to enrich the data in the existing <em>FrameNet-BR</em> database with some of the information available in <em>BabelNet</em>, more specifically the relations between various word senses.</p> <p>The project is going to be composed of two individual components encompassing two independent processes: an extraction tool and an annotation tool.</p> <p>The extraction tool is meant to utilize the available information in <em>FrameNet-BR</em> and <em>BabelNet</em> and generate a set of <em>hypotheses</em>, i.e. possible qualia relations between lexical units.</p> <p>The annotation tool is meant to provide a visual interface to a human annotator, so that they would be able to evaluate the quality of hypotheses generated by the extraction tool. Hypotheses of acceptable quality are expected to be added to the <em>FrameNet-BR</em> database.</p>

<p>Mobile Applications and smart devices are becoming an ever more important and ubiquitous part of our everyday lives. Mobile phones represented the first wave of smaller personal computers. And now, as the price of electronics and processing power continues to fall, there is an intersection between sensors and other electromechanical devices and computers that live on the edge of the Internet: close to the source of the data, processing the data locally and sending just what is required to other computers to consume. The vast growing applications of machine-to-machine technology, in general the Internet of Things (IoT), is increasing and changing the future of computing as well as lifestyle of the modern tech driven civilization. The MIT App Inventor along with Raspberry Pi can provide additional functionalities to the developers to build applications to the emerging market of IoT. The Raspberry Pi component in MIT App Inventor can benefit the current and future MIT App Inventor community to exercise their existing knowledge and skills to contribute to the emerging areas of IoT.</p>

OPNFV VSPERF already includes comprehensive Monitoring solution. However, it lacks the Log-Analysis and Alert-Management solution. This project aims to include log and alert management solutions based on opensources - ELK Stack. This will enhance the ability of VSPERF to: 1. Perform post-test correlation/causation analysis. 2. Adapt the Test-Traffic patterns based on the Alerts. The first aim is to create custom kibana dashboard analyzing and synthesizing VSPERF logs/events. For every test-run, VSPERF generates following logs: 1. vsperf-overall 2. vswitchd 3. trafficgen 4. host-cmds 5. guest-cmds These logs have useful information that can help user to understand the test behavior, and analyze the performance results. VSPERF will have Alerting from the Monitoring component, and lacks alerting from the logs. As the next step, this project aims to use the same information - The logs - and create alerts than will be notified to VSPERF.

<p><em>StatsD</em> is simple, text-based UDP protocol for receiving monitoring data of applications in architecture client-server. As of right now, there is no StatsD implementation for PCP available, other then <a href="https://github.com/lzap/pcp-mmvstatsd" target="_blank">this</a> which is not suitable for production environment.</p> <p>Goal of this project is to write PMDA agent for PCP in C, that would receive StatsD UDP packets and then aggregate and transfer handled data to PCP. There would be 3 basic types of metrics: counter, duration and gauge. Agent is to be build with modular architecture in mind with an option of changing implementation of both aggregator and parsers, which will allow to accurately describe differences between approaches to aggregation and text protocol parsing. Since the PMDA API is based on around callbacks the design has to be multithreaded.</p> <p>Agent is to be fully configurable with either PCP configuration options and/or separate configuration file. Writing integration tests is also in the scope of the project.</p>

Currently, TARDIS relies on IPyWidgets for its visualization modules. While effective within Jupyter environments, these widgets have limited compatibility with Sphinx, which is used to build and host the project’s documentation. In contrast, Panel provides significantly better integration with Sphinx, enabling seamless embedding of interactive components directly into static documentation. One of the key advantages of adopting Panel is the ability to deliver fully interactive visualizations within the documentation itself, without requiring a live Jupyter environment. This project aims to migrate TARDIS’s existing visualization tools from IPyWidgets to Panel and make them interactive in the documentation page itself. By doing so, users will be able to explore and interact with widgets directly on the documentation pages, leading to a more intuitive understanding of their functionality and usage. Additionally, this transition will establish a framework to allow future widgets to be easily integrated with interactivity in the documentation. Deliverables: - Custom Abundance Widget migrated to Panel - Grotrian Widget migrated to Panel - All widgets made interactive in the documentation.

This project aims to enhance RUXAILAB, an open-source platform for usability evaluation, by integrating three key components: eye tracking, sentiment analysis, and pre/post form tasks. These additions will support real-time, data-rich user testing and improve the understanding of user behavior and emotional responses. We will embed affordable eye tracking to visualize attention (e.g., heatmaps, gaze paths), implement multimodal sentiment analysis using facial expression recognition and NLP, and enable customizable pre/post-test forms to evaluate usability (e.g., SUS, NASA-TLX) and cognitive load. Built with a user-centered, modular, and API-first approach, the project will also define evaluation protocols, publish open datasets, and validate the system with real users. The outcome will be a comprehensive, extensible, and community-driven tool for empirical UX research, bridging qualitative and quantitative insights.

pg_stat_statements tracks query statistics across a PostgreSQL cluster, but its single-LWLock architecture creates a scalability bottleneck under high unique-query-rate workloads. When pg_stat_statements.max is undersized, deallocation and reset operations hold an exclusive lock while performing expensive operations (O(n log n) sort, full hash table iteration), blocking every backend on the server. My benchmarks on PG 19devel show 90-100% of active backends blocked on the pgss LWLock during deallocation churn. This project proposes a multi-part fix: (1) a pending-entry queue to avoid blocking on new entry insertion, (2) a restructured deallocation critical section that moves the sort outside the exclusive lock, (3) exploration of lock separation between structural changes and counter updates, and (4) an optimized reset path. Deliverables include a repeatable benchmark suite, patches for each optimization formatted for pgsql-hackers review, before/after performance measurements, and documentation.

This project aims to improve the robustness and flexibility of topic modeling in the CHAOSS Augur platform by enhancing its clustering_worker component. Currently, Augur relies on a static number of LDA topics, lacks model versioning, and does not support retraining based on data drift. To address these issues, this project will: Introduce dynamic topic number estimation via Gensim’s HDP model. Implement model versioning with model_id and timestamp support. Record model metadata including hyperparameters and coherence scores. Add automatic parameter tuning using coherence-based grid search. Detect data drift and trigger model retraining accordingly. Export rich visual outputs such as pyLDAvis and wordcloud images. Deliverables include upgraded database schema, a refactored topic modeling pipeline, metadata tables, Jupyter demo notebooks, and HTML visualizations for CHAOSS dashboards. These changes will make Augur’s insight engine more adaptive, interpretable, and future-proof.

<p>MacPorts currently uses a legacy version of Buildbot (0.8) as its continuous integration framework and hasn’t upgraded due to certain drawbacks in the Waterfall view of the newer versions. However, the currently deployed version is outdated and fails in several aspects due to the absence of some key features such as:</p> <ul> <li>The ability to write custom JavaScript views (UI components).</li> <li>Triggering a build whenever a pull request is made. Currently, a build can be started only when patches are committed to the master branch.</li> </ul> <p>This has led to some major setbacks with respect to developer productivity. MacPorts also needs some custom views in buildbot to be able to better analyze build history, commits, etc. The legacy version doesn’t allow us to write such custom views. This project will involve upgrading the Macports Buildbot infrastructure to the latest version, developing a plugin for buildbot and writing custom views.</p>

This project aims to systematically analyze and substantially improve KubeArmor's unit test coverage. It involves identifying untested code paths, designing and implementing new unit tests for crucial modules, and establishing a robust testing framework to boost code quality. Goals: 1. Measure Coverage: Accurately measure and report current unit test coverage for all Go packages. 2. Prioritized Test Implementation: Write new unit tests, prioritizing core modules like core, monitor, enforcer, log, and feeder, and addressing other packages with no test files. 3. Identify Untested Components: Pinpoint major untested functionalities and propose specific test scenarios. 4. Achieve Measurable Improvement: Submit pull requests that significantly increase overall unit test coverage. Expected Outcome: A detailed coverage report, a suite of new, effective unit tests, and a measurable improvement in test coverage, leading to enhanced code quality and reliability.