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30 Dienstag 14 3 2023 Messe-News Static code analysis Can AI ML encourage devs to adopt static analysis testing? Machine learning for artificial intelligence empowers developers to adopt static analysis testing techniques for maximum benefit By Igor Kirilenko Testing acceleration goes hand in hand with artificial intelligence and machine learning This isn’t about androids doing our laundry It’s about programs that learn over time to enhance processes already in place For instance say you order from three different restaurants on a food delivery app in one week after searching for them specifically The next time you log in to that app it may now recommend you reorder from those same restaurants because you ordered from them previously This process of learning and adapting to the user is exactly how AI and machine learning for static analysis works It just involves identifying and prioritizing code violations as opposed to ordering your favorite shawarma Protip AI with machine learning for static analysis will make the process simpler and less stressful So here’s exactly how to do that by answering the following questions • What are the challenges of adopting static analysis? • Can static analysis be automated? • Why does the adoption of static analysis seem difficult and expensive? • How does machine learning help static analysis testing? • What do AI and machine learning techniques mean for your SDLC? Challenges of adopting static analysis Static analysis is used to find vulnerabilities in code often against industry coding and security standards such as OWASP CWE and others Developers are often not equipped to analyze their own code for these issues or to identify and prioritize what fixes are needed It’s true that there is no shortcut or “easy mode” for static analysis testing You must do it regularly and thoroughly to provide the most utility However automating static analysis testing and leveraging machine learning can enhance your results and make things much easier for your developers Can static analysis be automated? Definitely Static analysis identifies defects and errors in your source code In fact automating static analysis testing via tools further enhances the results you get While the types of analysis and priorities can differ the way the SA tool works and applying its methodology are the same For example the various analyses available revolve around four key aspects • Security Locate vulnerabilities that increase security risks • Reliability Locate issues that can lead to problems such as memory leaks • Performance Locate errors that reduce performance • Style Audit the code to help developers adopt uniform coding styles Automating these processes in a continuous manner helps teams manage workflows better by identifying potential issues before they become big problems Why is the adoption of static analysis difficult and expensive? The reasons why many developers view adopting static analysis as both expensive and daunting come down to project scope and approach Many teams want to tackle what they feel are the most pressing issues first but also tend to bite off more than they can chew at that time Instead tackle the most significant problems first and limit yourself to one “bite” at a time However it should be noted that a “baby step” should not become a stopping point Safetycritical industries require addressing all violations to establish compliance before a product can be released In the meantime this step helps prevent your team from being overwhelmed with thousands of violations all at once How AI machine learning helps static analysis Static analysis is about detecting problems before you even compile and execute the code But AI can be used to help across multiple levels of software testing such as • UI tests Manage and maintain volatile automated UI testing and optimize the execution of manual tests • API tests Discover API usage patterns and automatically generate complete test scenarios • Unit tests Achieve and maintain code coverage especially for modified code • Code analysis reliability and security Fight violations in the code base Artificial intelligence helps teams create and maintain automated tests Moreover it can optimize test execution and maximize actionable results delivery by augmenting your processes in several ways hs Parasoft Deutschland Hall 4 Stand 378 Im ag e Pa ra so ft Im ag e Pa ra so ft The software testing pyramid AI can be used to support software testing across all levels – Vom Spezialisten für Spezialisten Der ideale „Distribution-Antrieb“ für Ihre Produktentwicklungen und Projekte Lust auf Popcorn? Frisch a n unsere r „SUPPO RT-COR N“ Maschin e Halle 3A - 319 Igor Kirilenko is Parasoft’s VP of Development and responsible for technical strategy architecture and development of Parasoft products Igor brings over 20 years of experience in leading engineering teams with a specialization in establishing and promoting the best agile practices in software development environments