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• Our latest improvement to QA : Screenshot Testing

  2 octobre 2013, par benaka — Development

  Introduction to QA in Piwik

  Like any piece of good software, Piwik comes with a comprehensive QA suite that includes unit and integration tests. The unit tests make sure core components of Piwik work properly. The integration tests make sure Piwik’s tracking and report aggregation and APIs work properly.

  To complete our QA suite, we’ve recently added a new type of tests : Screenshot tests, that we use to make sure Piwik’s controller and JavaScript code works properly.

  This blog post will explain how they work and describe our experiences setting them up ; we hope to show you an example of innovative QA practices in an active open source project.

  Screenshot Tests

  As the name implies, our screenshot tests (1) first capture a screenshot of a URL, then (2) compare the result with an expected image. This lets us test the code in Piwik’s controllers and Piwik’s JavaScript simply by specifying a URL.

  Contrast this with conventional UI tests that test for page content changes. Such tests require writing large amounts of test code that, at most, check for changes in HTML. Our tests, on the otherhand, will be able to show regressions in CSS and JavaScript rendering logic with a bare minimum of testing code.

  Capturing Screenshots

  Screenshots are captured using a 3rd party tool. We tried several tools before settling on PhantomJS. PhantomJS executes a JavaScript file with an environment that allows it to create WebKit powered web views. When capturing a screenshot, we supply PhantomJS with a script that :

  • opens a web page view,
  • loads a URL,
  • waits for all AJAX requests to be completed,
  • waits for all images to be loaded
  • waits for all JavaScript to be run.

  Then it renders the completed page to an PNG file.

  • To see how we use PhantomJS see capture.js.
  • To see how we wait for AJAX requests to complete and images to load see override.js.

  Comparing Screenshots

  Once a screenshot is generated we test for UI regressions by comparing it with an expected image. There is no sort of fuzzy matching involved. We just check that the images consist of the same bytes.

  If a screenshot test fails we use ImageMagick’s compare command line tool to generate an image diff :

  

  In this example above, there was a change that caused the Search box to be hidden in the datatable. This resulted in the whole Data table report being shifted up a few pixels. The differences are visible in red color which gives rapid feedback to the developers what has changed in the last commit.

  Screenshot Tests on Travis

  We experienced trouble generating identical screenshots on different machines, so our tests were not initially automated by Travis. Once we surpassed this hurdle, we created a new github repo to store our UI tests and screenshots and then enabled the travis build for it. We also made sure that every time a commit is pushed to the Piwik repo, our travis build will push a commit to the UI test repo to run the UI tests.

  We decided to create a new repository so the main repository wouldn’t be burdened with the large screenshot files (which git would not handle very well). We also made sure the travis build would upload all the generated screenshots to a server so debugging failures would be easier.

  Problems we experienced

  Getting generated screenshots to render identically on separate machines was quite a challenge. It took months to figure out how to get it right. Here’s what we learned :

  Fonts will render identically on different machines, but different machines can pick the wrong fonts. When we first tried getting these tests to run on Travis, we noticed small differences in the way fonts were rendered on different machines. We thought this was an insurmountable problem that would occur due to the libraries installed on these machines. It turns out, the machines were just picking the wrong fonts. After installing certain fonts during our Travis build, everything started working.

  Different versions of GD can generate slightly different images. GD is used in Piwik to, among other things, generate sparkline images. Different versions of GD will result in slightly different images. They look the same to the naked eye, but some pixels will have slightly different colors. This is, unfortunately, a problem we couldn’t solve. We couldn’t make sure that everyone who runs the tests uses the same version of GD, so instead we disabled sparklines for UI testing.

  What we learned about existing screenshot capturing tools

  We tried several screenshot capturing tools before finding one that would work adequately. Here’s what we learned about them :

  • CutyCapt This is the first screenshot capturing tool we tried. CutyCapt is a C++ program that uses QtWebKit to load and take a screenshot of a page. It can’t be used to capture multiple screenshots in one run and it can’t be used to wait for all AJAX/Images/JavaScript to complete/load (at least not currently).

  • PhantomJS This is the solution we eventually chose. PhantomJS is a headless scriptable browser that currently uses WebKit as its rendering engine.

    For the most part, PhantomJS is the best solution we found. It reliably renders screenshots, allows JavaScript to be injected into pages it loads, and since it essentially just runs JavaScript code that you provide, it can be made to do whatever you want.

  • SlimerJS SlimerJS is a clone of PhantomJS that uses Gecko as the rendering engine. It is meant to function similarly to PhantomJS. Unfortunately, due to some limitations hard-coded in Mozilla’s software, we couldn’t use it.

    For one, SlimerJS is not headless. There is, apparently, no way to do that when embedding Mozilla. You can, however, run it through xvfb, however the fact that it has to create a window means some odd things can happen. When using SlimerJS, we would sometimes end up with images where tooltips would display as if the mouse was hovering over an element. This inconsistency meant we couldn’t use it for our tests.

  One tool we didn’t try was Selenium Webdriver. Although Selenium is traditionally used to create tests that check for HTML content, it can be used to generate screenshots. (Note : PhantomJS supports using a remote WebDriver.)

  Our Future Plans for Screenshot Testing

  At the moment we render a couple dozen screenshots. We test how our PHP code, JavaScript code and CSS makes Piwik’s UI look, but we don’t test how it behaves. This is our next step.

  We want to create Screenshot Unit Tests for each UI control Piwik uses (for example, the Data Table View or the Site Selector). These tests would use the Widgetize plugin to load a control by itself, then execute JavaScript that simulates events and user behavior, and finally take a screenshot. This way we can test how our code handles clicks and hovers and all sorts of other behavior.

  Screenshots Tests will make Piwik more stable and keep us agile and able to release early and often. Thank you for your support & Spreading the word about Piwik !

• How HSBC and ING are transforming banking with AI

  9 novembre 2024, par Daniel Crough — Banking and Financial Services, Featured Banking Content

  We recently partnered with FinTech Futures to produce an exciting webinar discussing how analytics leaders from two global banks are using AI to protect customers, streamline operations, and support environmental goals.

  Watch the on-demand webinar : Advancing analytics maturity.

  Email

  By providing your email and clicking “submit”, you agree to receive direct marketing materials relating to Matomo products and services, surveys, information about events, publications and promotions. You can unsubscribe at any time by clicking the opt-out link provided in each communication. We will process your personal information in accordance with our Privacy Policy.

  
  
  
  
  

  Δ

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&lt;/script&gt;

  Meet the expert panel

  Roshini Johri heads ESG Analytics at HSBC, where she leads AI and remote sensing applications supporting the bank’s net zero goals. Her expertise spans climate tech and financial services, with a focus on scalable analytics solutions.

   

  Marco Li Mandri leads Advanced Analytics Strategy at ING, where he focuses on delivering high-impact solutions and strengthening analytics foundations. His background combines analytics, KYC operations, and AI strategy.

   

  Carmen Soini Tourres works as a Web Analyst Consultant at Matomo, helping financial organisations optimise their digital presence whilst maintaining privacy compliance.

   

  Key findings from the webinar

  The discussion highlighted four essential elements for advancing analytics capabilities :

  1. Strong data foundations matter most

  “It doesn’t matter how good the AI model is. It is garbage in, garbage out,”

  Johri explained. Banks need robust data governance that works across different regulatory environments.

  2. Transform rather than tweak

  Li Mandri emphasised the need to reconsider entire processes :

  “We try to look at the banking domain and processes and try to re-imagine how they should be done with AI.”

  3. Bridge technical and business understanding

  Both leaders stressed the value of analytics translators who understand both technology and business needs.

  “We’re investing in this layer we call product leads,”

  Li Mandri explained. These roles combine technical knowledge with business acumen – a rare but vital skill set.

  4. Consider production costs early

  Moving from proof-of-concept to production requires careful planning. As Johri noted :

  “The scale of doing things in production is quite massive and often doesn’t get accounted for in the cost.”

  This includes :

  • Ongoing monitoring requirements
  • Maintenance needs
  • Regulatory compliance checks
  • Regular model updates

  Real-world applications

  ING’s approach demonstrates how banks can transform their operations through thoughtful AI implementation. Li Mandri shared several areas where the bank has successfully deployed analytics solutions, each benefiting both the bank and its customers.

  Customer experience enhancement

  The bank’s implementation of AI-powered instant loan processing shows how analytics can transform traditional banking.

  “We know AI can make loans instant for the customer, that’s great. Clicking one button and adding a loan, that really changes things,”

  Li Mandri explained. This goes beyond automation – it represents a fundamental shift in how banks serve their customers.

  The system analyses customer data to make rapid lending decisions while maintaining strong risk assessment standards. For customers, this means no more lengthy waiting periods or complex applications. For the bank, it means more efficient resource use and better risk management.

  The bank also uses AI to personalise customer communications.

  “We’re using that to make certain campaigns more personalised, having a certain tone of voice,”

  noted Li Mandri. This particularly resonates with younger customers who expect relevant, personalised interactions from their bank.

  Operational efficiency transformation

  ING’s approach to Know Your Customer (KYC) processes shows how AI can transform resource-heavy operations.

  “KYC is a big area of cost for the bank. So we see massive value there, a lot of scale,”

  Li Mandri explained. The bank developed an AI-powered system that :

  • Automates document verification
  • Flags potential compliance issues for human review
  • Maintains consistent standards across jurisdictions
  • Reduces processing time while improving accuracy

  This implementation required careful consideration of regulations across different markets. The bank developed monitoring systems to ensure their AI models maintain high accuracy while meeting compliance standards.

  In the back office, ING uses AI to extract and process data from various documents, significantly reducing manual work. This automation lets staff focus on complex tasks requiring human judgment.

  Sustainable finance initiatives

  ING’s commitment to sustainable banking has driven innovative uses of AI in environmental assessment.

  “We have this ambition to be a sustainable bank. If you want to be a sustainable finance customer, that requires a lot of work to understand who the company is, always comparing against its peers.”

  The bank developed AI models that :

  • Analyse company sustainability metrics
  • Compare environmental performance against industry benchmarks
  • Assess transition plans for high-emission industries
  • Monitor ongoing compliance with sustainability commitments

  This system helps staff evaluate the environmental impact of potential deals quickly and accurately.

  “We are using AI there to help our frontline process customers to see how green that deal might be and then use that as a decision point,”

  Li Mandri noted.

  HSBC’s innovative approach

  Under Johri’s leadership, HSBC has developed several groundbreaking uses of AI and analytics, particularly in environmental monitoring and operational efficiency. Their work shows how banks can use advanced technology to address complex global challenges while meeting regulatory requirements.

  Environmental monitoring through advanced technology

  HSBC uses computer vision and satellite imagery analysis to measure environmental impact with new precision.

  “This is another big research area where we look at satellite images and we do what is called remote sensing, which is the study of a remote area,”

  Johri explained.

  The system provides several key capabilities :

  • Analysis of forest coverage and deforestation rates
  • Assessment of biodiversity impact in specific regions
  • Monitoring of environmental changes over time
  • Measurement of environmental risk in lending portfolios

  “We can look at distant images of forest areas and understand how much percentage deforestation is being caused in that area, and we can then measure our biodiversity impact more accurately,”

  Johri noted. This technology enables HSBC to :

  • Make informed lending decisions
  • Monitor environmental commitments of borrowers
  • Support sustainability-linked lending programmes
  • Provide accurate environmental impact reporting

  Transforming document analysis

  HSBC is tackling one of banking’s most time-consuming challenges : processing vast amounts of documentation.

  “Can we reduce the onus of human having to go and read 200 pages of sustainability reports each time to extract answers ?”

  Johri asked. Their solution combines several AI technologies to make this process more efficient while maintaining accuracy.

  The bank’s approach includes :

  • Natural language processing to understand complex documents
  • Machine learning models to extract relevant information
  • Validation systems to ensure accuracy
  • Integration with existing compliance frameworks

  “We’re exploring solutions to improve our reporting, but we need to do it in a safe, robust and transparent way.”

  This careful balance between efficiency and accuracy exemplifies HSBC’s approach to AI.

  Building future-ready analytics capabilities

  Both banks emphasise that successful analytics requires a comprehensive, long-term approach. Their experiences highlight several critical considerations for financial institutions looking to advance their analytics capabilities.

  Developing clear governance frameworks

  “Understanding your AI risk appetite is crucial because banking is a highly regulated environment,”

  Johri emphasised. Banks need to establish governance structures that :

  • Define acceptable uses for AI
  • Establish monitoring and control mechanisms
  • Ensure compliance with evolving regulations
  • Maintain transparency in AI decision-making

  Creating solutions that scale

  Li Mandri stressed the importance of building systems that grow with the organisation :

  “When you try to prototype a model, you have to take care about the data safety, ethical consideration, you have to identify a way to monitor that model. You need model standard governance.”

  Successful scaling requires :

  • Standard approaches to model development
  • Clear evaluation frameworks
  • Simple processes for model updates
  • Strong monitoring systems
  • Regular performance reviews

  Investing in people and skills

  Both leaders highlighted how important skilled people are to analytics success.

  “Having a good hiring strategy as well as creating that data literacy is really important,”

  Johri noted. Banks need to :

  • Develop comprehensive training programmes
  • Create clear career paths for analytics professionals
  • Foster collaboration between technical and business teams
  • Build internal expertise in emerging technologies

  Planning for the future

  Looking ahead, both banks are preparing for increased regulation and growing demands for transparency. Key focus areas include :

  • Adapting to new privacy regulations
  • Making AI decisions more explainable
  • Improving data quality and governance
  • Strengthening cybersecurity measures

  Practical steps for financial institutions

  The experiences shared by HSBC and ING provide valuable insights for financial institutions at any stage of their analytics journey. Their successes and challenges outline a clear path forward.

  Key steps for success

  Financial institutions looking to enhance their analytics capabilities should :

  1. Start with strong foundations
     • Invest in clear data governance frameworks
     • Set data quality standards
     • Build thorough documentation processes
     • Create transparent data tracking
  2. Think strategically about AI implementation
     • Focus on transformative rather than small changes
     • Consider the full costs of AI projects
     • Build solutions that can grow
     • Balance innovation with risk management
  3. Invest in people and processes
     • Develop internal analytics expertise
     • Create clear paths for career growth
     • Foster collaboration between technical and business teams
     • Build a culture of data literacy
  4. Plan for scale
     • Establish monitoring systems
     • Create governance frameworks
     • Develop standard approaches to model development
     • Stay flexible for future regulatory changes

  Learn more

  Want to hear more insights from these industry leaders ? Watch the complete webinar recording on demand. You’ll learn :

  • Detailed technical insights from both banks
  • Extended Q&A with the speakers
  • Additional case studies and examples
  • Practical implementation advice

   

   

  Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

  Watch the on-demand webinar : Advancing analytics maturity.

  Email

  By providing your email and clicking “submit”, you agree to receive direct marketing materials relating to Matomo products and services, surveys, information about events, publications and promotions. You can unsubscribe at any time by clicking the opt-out link provided in each communication. We will process your personal information in accordance with our Privacy Policy.

  
  
  
  
  

  Δ

  <script>document.getElementById( "ak_js_4" ).setAttribute( "value", ( new Date() ).getTime() );</script>

  &lt;script&gt;<br />
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&lt;/script&gt;

• Empty audio backends for torchaudio list audio backends, with ffmpeg installed on system libraries [closed]

  28 mars, par Alberto Agudo Dominguez

  I installed torchaudio 2.5.1 and a system install of ffmpeg on Windows and get :

  


  PS C:\Users\> ffmpeg -version
ffmpeg version 2025-01-05-git-19c95ecbff-essentials_build-www.gyan.dev Copyright (c) 2000-2025 the FFmpeg developers
built with gcc 14.2.0 (Rev1, Built by MSYS2 project)
configuration: --enable-gpl --enable-version3 --enable-static --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-bzlib --enable-lzma --enable-zlib --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-sdl2 --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxvid --enable-libaom --enable-libopenjpeg --enable-libvpx --enable-mediafoundation --enable-libass --enable-libfreetype --enable-libfribidi --enable-libharfbuzz --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-dxva2 --enable-d3d11va --enable-d3d12va --enable-ffnvcodec --enable-libvpl --enable-nvdec --enable-nvenc --enable-vaapi --enable-libgme --enable-libopenmpt --enable-libopencore-amrwb --enable-libmp3lame --enable-libtheora --enable-libvo-amrwbenc --enable-libgsm --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enable-librubberband
libavutil      59. 54.101 / 59. 54.101
libavcodec     61. 31.100 / 61. 31.100
libavfilter    10.  6.101 / 10.  6.101
libswresample   5.  4.100 /  5.  4.100
libpostproc    58.  4.100 / 58.  4.100

PS C:\Users\> python
Python 3.12.5 (tags/v3.12.5:ff3bc82, Aug  6 2024, 20:45:27) [MSC v.1940 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import torchaudio
>>> torchaudio.list_audio_backends()
[]


  


  Hence ffmpeg is added to the path and recognized by the console, but not by torchaudio.