What is «static code analysis»? It is a technique that allows, at the same time with unit-tests, dynamic code analysis, code review and others, to increase code quality, increase its reliability and decrease the development time.
PVS-Studio is a static code analyzer for C, C++, C#, and Java that detects bugs and vulnerabilities. It supports various compilers and IDE plugins. It uses data flow analysis, symbolic execution, pattern matching, and other techniques to detect bugs like buffer overflows, leaks, dead code, and undefined behavior. Over 700 diagnostics are implemented to date across the supported languages. The analyzer produces warnings classified by standard taxonomies. Users can exclude files, suppress warnings, and integrate it with continuous integration systems. Support and documentation is provided through online and PDF references.
Static code analysis involves using tools to analyze source code for potential issues. It can find bugs, code quality issues, and other problems but is not a replacement for testing. Several experts note that combining static analysis, inspections, and testing leads to better defect removal than only using testing. Common static analysis tools include FxCop, StyleCop, ReSharper, and NDepend. Integrating static analysis into the development process can provide benefits but obstacles like resources and unrealistic expectations must be addressed.
Static source code analysis tools can help developers find bugs early by analyzing code without executing it. The document recommends several free, open source tools for different programming languages that can find security issues, reliability problems, and other bugs. It emphasizes that while tools are useful, manual code reviews by experts are still needed, as no tool can find all issues or guarantee code is bug-free.
Code coverage is a measure of how much source code is covered during testing. It is not a goal in itself but can be used pragmatically to improve testing in several ways. Coverage data should be filtered and combined with other metrics to prioritize test development and focus on the most important or risky code. While 100% coverage may not be needed or prove quality, coverage is a useful tool when used properly along with other techniques rather than in isolation or as the only metric.
PVS-Studio advertisement - static analysis of C/C++ codeAndrey Karpov
This document advertises the PVS-Studio static analyzer. It describes how using PVS-Studio reduces the number of errors in code of C/C++/C++11 projects and costs on code testing, debugging and maintenance. A lot of examples of errors are cited found by the analyzer in various Open-Source projects. The document describes PVS-Studio at the time of version 4.38 on October 12-th, 2011, and therefore does not describe the capabilities of the tool in the next versions. To learn about new capabilities, visit the product's site <a>http://www.viva64.com</a> or search for an updated version of this article.
PVS-Studio is a static code analyzer that checks C, C++ and C# code for bugs. It supports projects developed with Windows (Visual Studio) and Linux (Clang, GCC). It integrates with tools like Visual Studio, SonarQube and supports standalone use. PVS-Studio detects many types of bugs like null pointer dereferences, uninitialized variables, dead code, buffer overflows, security issues and more. It has been effective at finding real bugs in major open source projects.
This document discusses several Java code quality tools, including CodeProAnalytix for code analysis and metrics, PMD for detecting bugs and suboptimal code, FindBugs for finding correctness bugs and bad practices, and Cobertura/EMMA for code coverage analysis. It also mentions Checkstyle for checking code against coding standards, Tattletale for dependency analysis, and consolidated tools like Sonar and Xradar. Finally, it describes UCDetector for finding unnecessary public code.
Server Side Template Injection by Mandeep JadonMandeep Jadon
This document discusses server-side template injection (SSTI) vulnerabilities that can allow remote code execution on modern web applications. It begins with an introduction to templating engines and SSTI vulnerabilities. It then covers detecting, identifying, and exploiting SSTI vulnerabilities, providing examples using the Python Flask framework. It concludes with recommendations for preventing SSTI, such as not allowing user-modified templates and executing user code in a restricted sandbox.
Parasoft provides automated quality systems to help companies achieve FDA software compliance. It offers integrated defect prevention and detection technologies like static code analysis, dynamic analysis, code inspections, and automated unit testing. These technologies automate various software validation best practices recommended by the FDA. Parasoft's solutions can find bugs, analyze code quality, review code, generate test cases, track testing coverage, and manage the software development lifecycle to improve quality and ensure traceability.
SonarQube is an open platform to manage code quality. It has got a very efficient way of navigating, a balance between high-level view, dashboard, TimeMachine and defect hunting tools.
SonarQube tool is a web-based application. Rules, alerts, thresholds, exclusions, settings… can be configured online.
PVS-Studio is ready to improve the code of Tizen operating systemAndrey Karpov
Objective. Contract agreement with PVS-Studio team concerning the error fixing and regular code audit.
Currently, PVS-Studio detects more than 10% of errors that are present in the code of the Tizen project.
In the case of regular use of PVS-Studio on the new code, about 20% of errors can be prevented.
I predict that PVS-Studio team can detect and fix about 27 000 errors in the Tizen project.
It’s not easy to perform a good code review. Often done in a hurry just to get it done, it only makes things worse. People treat it as an obstacle, not a helpful thing. I am gonna tell you why code reviews are important and how they can help you maintain good quality code. I will not only tell who are the code reviews for, how to raise a useful code review, but also how to perform a good code review in the quickest time possible.
At some moment, long ago, we somehow started to cover in our articles any subject but the PVS-Studio tool itself. We told you about the projects we checked and the C++ language's subtle details; we told you how to create plugins in C# or how to launch PVS-Studio from the command line... But PVS-Studio is first of all meant for developers working in Visual Studio. We've done quite a lot to make it easier and more comfortable for them to use our tool. Yet this particular aspect usually stays off screen. Now I decided to improve that and tell you about the PVS-Studio plugin from scratch. If you are a Visual C++ user, this article is for you.
This document discusses using the Roslyn compiler API to build .NET static code analyzers. It begins with an overview of existing free and open source .NET static analysis tools. It then covers the basics of the Roslyn API and how to create a code analyzer that checks for weak password lengths in ASP.NET Identity. It also discusses challenges with analyzing non-code files and demonstrates a tool called Puma Scan that contains over 40 security rules for .NET applications. The document encourages contributions to help expand analysis capabilities and rule coverage.
This document discusses program analysis methods and the Valgrind tool. It begins with an introduction to program analysis, including dynamic and static analysis. It then provides an example of bad code containing bugs and demonstrates how Valgrind can detect these bugs, such as invalid reads, writes, and memory leaks. The document also covers some limitations of Valgrind and describes additional tools in Valgrind for profiling and thread error detection. It concludes with sections on how Valgrind works using binary translation and an intermediate representation, as well as tuning Valgrind through suppression files and client requests.
Static Code Analysis for Projects, Built on Unreal EngineAndrey Karpov
Why Do You Need Static Analysis? Detect errors early in the program development process. Get recommendations on code formatting. Check your spelling. Calculate various software metrics.
The document discusses code review tools and processes. It describes formal in-person code review meetings, challenges with scheduling and concentrating, and more efficient alternatives like email and web-based code review tools. It also covers motivations for code review like finding defects early and improving coding standards, and tips for effective code reviews like preparing, focusing on understanding rather than criticism, and keeping reviews short.
The document discusses best practices for code review of secure web applications. It covers strategies like manual review using checklists focused on authentication, authorization, session management, input validation, output sanitization, and other topics. Sample code snippets are provided to demonstrate proper and insecure implementations for these areas. The checklist topics to be covered in the next meeting include preventing cross-site request forgery, implementing cryptographic controls, handling errors properly, logging appropriately, and avoiding race conditions.
IoT 개발자를 위한 Embedded C에서 Test Coverage를 추출해보자Taeyeop Kim
gcov is a tool that reports code coverage statistics when used with GCC. It shows which lines and sections of code were executed and which were not. lcov is a graphical front-end for gcov that produces HTML reports of code coverage. CppUTest is a C/C++ unit testing framework that can be configured to work with gcov to produce code coverage reports when tests are run.
Making Your Own Static Analyzer Using Freud DSL. Marat VyshegorodtsevYandex
This document discusses the challenges of security testing and how development-driven security testing can help address them. It outlines three main reasons security testing is difficult: large scope, difficulty hooking tests into code, and the halting problem. It then demonstrates how tools like Freud and PowerMock can help security testers iterate over code, write tests for unsafe calls and missing implementations, find code calling dangerous APIs to fuzz, and test for forbidden parameters - addressing problems that regular testing can miss due to unintended behaviors. By working with developers using techniques like these, security testing can be more comprehensive.
This document provides an overview of test-driven development (TDD) techniques including unit testing with JUnit, behavior-driven development (BDD) with Cucumber, test automation, and using mock objects with Mockito. It defines TDD and its processes, describes tools like JUnit, Cucumber and Mockito, and provides references for further reading on testing practices.
Overview of PVS-studio analyzer advanced features. PVS-Studio Standalone is an IDE/compiler independent tool which allows you to analyze your code and review analysis results.
- The document proposes a technique to help developers debug code by detecting similar code elements between a developer's code and code found in answers to questions on Stack Overflow.
- The technique involves detecting code clones between the developer's code and code in Stack Overflow questions and answers, then filtering the results to find the most similar code elements.
- An evaluation on several open source projects found the technique was able to detect 189 warnings, with 171 warnings confirmed as real bugs by developers.
There exist many code review checklist blogs/articles in the net telling standard Java best practices/guidelines; And I have come up with a checklist accommodating best practices from some of the recent popular books (reference given) and guidelines from Oracle (on Security).
This document provides an introduction to binary exploitation. It outlines the course, which will cover basic stack overflows, shellcode injection, and exploit mitigation technologies. It explains how buffer overflows can be used to overwrite the return address and change the flow of execution. By injecting shellcode into the buffer and overwriting the return address to point to it, arbitrary code can be executed to gain unauthorized access. Modern defenses like ASLR and NX are discussed, as well as future topics like return-oriented programming and format string vulnerabilities. The overall goal is to understand software exploitation and how to identify vulnerabilities in programs.
The document discusses code review practices at the presenter's team. It covers basic concepts like broken window theory and technical debt. It then details the team's Git and Jira processes, coding standards using PHP CodeSniffer, and code review steps involving checking code, unit tests, and identifying inefficiencies and improvements. The concepts discussed will become obsolete when applications can self-improve by writing new code to meet business needs without human involvement.
This document discusses static code analysis and tools like SonarQube and Coverity. Static code analysis examines code without executing it to find bugs. Monitoring and fixing code quality issues improves application quality and delivery. SonarQube is an open source tool that manages code quality through analysis, issues detection, and metrics. Coverity also detects defects early through static analysis of various languages. Both tools help improve code quality.
Verification at scale: Fitting static code analysis into continuous integrationRogue Wave Software
Static code analysis (SCA) is a decades-proven software verification method that’s become essential for many development teams. With the growing adoption of DevOps processes and CI tools, it’s even more important that those familiar with and new to SCA understand how it fits into modern processes to maximize its benefits.
This talk describes three different ways of approaching static code analysis and explains the advantages and disadvantages of each, including test coverage, performance, and standards compliance. Starting with older server-based and desktop-based analysis, followed by the latest continuous static analysis for CI, you will walk away with an understanding of the different types of SCA and how to choose the best option that fits your team’s processes, environment, and release schedules.
This document discusses several Java code quality tools, including CodeProAnalytix for code analysis and metrics, PMD for detecting bugs and suboptimal code, FindBugs for finding correctness bugs and bad practices, and Cobertura/EMMA for code coverage analysis. It also mentions Checkstyle for checking code against coding standards, Tattletale for dependency analysis, and consolidated tools like Sonar and Xradar. Finally, it describes UCDetector for finding unnecessary public code.
Server Side Template Injection by Mandeep JadonMandeep Jadon
This document discusses server-side template injection (SSTI) vulnerabilities that can allow remote code execution on modern web applications. It begins with an introduction to templating engines and SSTI vulnerabilities. It then covers detecting, identifying, and exploiting SSTI vulnerabilities, providing examples using the Python Flask framework. It concludes with recommendations for preventing SSTI, such as not allowing user-modified templates and executing user code in a restricted sandbox.
Parasoft provides automated quality systems to help companies achieve FDA software compliance. It offers integrated defect prevention and detection technologies like static code analysis, dynamic analysis, code inspections, and automated unit testing. These technologies automate various software validation best practices recommended by the FDA. Parasoft's solutions can find bugs, analyze code quality, review code, generate test cases, track testing coverage, and manage the software development lifecycle to improve quality and ensure traceability.
SonarQube is an open platform to manage code quality. It has got a very efficient way of navigating, a balance between high-level view, dashboard, TimeMachine and defect hunting tools.
SonarQube tool is a web-based application. Rules, alerts, thresholds, exclusions, settings… can be configured online.
PVS-Studio is ready to improve the code of Tizen operating systemAndrey Karpov
Objective. Contract agreement with PVS-Studio team concerning the error fixing and regular code audit.
Currently, PVS-Studio detects more than 10% of errors that are present in the code of the Tizen project.
In the case of regular use of PVS-Studio on the new code, about 20% of errors can be prevented.
I predict that PVS-Studio team can detect and fix about 27 000 errors in the Tizen project.
It’s not easy to perform a good code review. Often done in a hurry just to get it done, it only makes things worse. People treat it as an obstacle, not a helpful thing. I am gonna tell you why code reviews are important and how they can help you maintain good quality code. I will not only tell who are the code reviews for, how to raise a useful code review, but also how to perform a good code review in the quickest time possible.
At some moment, long ago, we somehow started to cover in our articles any subject but the PVS-Studio tool itself. We told you about the projects we checked and the C++ language's subtle details; we told you how to create plugins in C# or how to launch PVS-Studio from the command line... But PVS-Studio is first of all meant for developers working in Visual Studio. We've done quite a lot to make it easier and more comfortable for them to use our tool. Yet this particular aspect usually stays off screen. Now I decided to improve that and tell you about the PVS-Studio plugin from scratch. If you are a Visual C++ user, this article is for you.
This document discusses using the Roslyn compiler API to build .NET static code analyzers. It begins with an overview of existing free and open source .NET static analysis tools. It then covers the basics of the Roslyn API and how to create a code analyzer that checks for weak password lengths in ASP.NET Identity. It also discusses challenges with analyzing non-code files and demonstrates a tool called Puma Scan that contains over 40 security rules for .NET applications. The document encourages contributions to help expand analysis capabilities and rule coverage.
This document discusses program analysis methods and the Valgrind tool. It begins with an introduction to program analysis, including dynamic and static analysis. It then provides an example of bad code containing bugs and demonstrates how Valgrind can detect these bugs, such as invalid reads, writes, and memory leaks. The document also covers some limitations of Valgrind and describes additional tools in Valgrind for profiling and thread error detection. It concludes with sections on how Valgrind works using binary translation and an intermediate representation, as well as tuning Valgrind through suppression files and client requests.
Static Code Analysis for Projects, Built on Unreal EngineAndrey Karpov
Why Do You Need Static Analysis? Detect errors early in the program development process. Get recommendations on code formatting. Check your spelling. Calculate various software metrics.
The document discusses code review tools and processes. It describes formal in-person code review meetings, challenges with scheduling and concentrating, and more efficient alternatives like email and web-based code review tools. It also covers motivations for code review like finding defects early and improving coding standards, and tips for effective code reviews like preparing, focusing on understanding rather than criticism, and keeping reviews short.
The document discusses best practices for code review of secure web applications. It covers strategies like manual review using checklists focused on authentication, authorization, session management, input validation, output sanitization, and other topics. Sample code snippets are provided to demonstrate proper and insecure implementations for these areas. The checklist topics to be covered in the next meeting include preventing cross-site request forgery, implementing cryptographic controls, handling errors properly, logging appropriately, and avoiding race conditions.
IoT 개발자를 위한 Embedded C에서 Test Coverage를 추출해보자Taeyeop Kim
gcov is a tool that reports code coverage statistics when used with GCC. It shows which lines and sections of code were executed and which were not. lcov is a graphical front-end for gcov that produces HTML reports of code coverage. CppUTest is a C/C++ unit testing framework that can be configured to work with gcov to produce code coverage reports when tests are run.
Making Your Own Static Analyzer Using Freud DSL. Marat VyshegorodtsevYandex
This document discusses the challenges of security testing and how development-driven security testing can help address them. It outlines three main reasons security testing is difficult: large scope, difficulty hooking tests into code, and the halting problem. It then demonstrates how tools like Freud and PowerMock can help security testers iterate over code, write tests for unsafe calls and missing implementations, find code calling dangerous APIs to fuzz, and test for forbidden parameters - addressing problems that regular testing can miss due to unintended behaviors. By working with developers using techniques like these, security testing can be more comprehensive.
This document provides an overview of test-driven development (TDD) techniques including unit testing with JUnit, behavior-driven development (BDD) with Cucumber, test automation, and using mock objects with Mockito. It defines TDD and its processes, describes tools like JUnit, Cucumber and Mockito, and provides references for further reading on testing practices.
Overview of PVS-studio analyzer advanced features. PVS-Studio Standalone is an IDE/compiler independent tool which allows you to analyze your code and review analysis results.
- The document proposes a technique to help developers debug code by detecting similar code elements between a developer's code and code found in answers to questions on Stack Overflow.
- The technique involves detecting code clones between the developer's code and code in Stack Overflow questions and answers, then filtering the results to find the most similar code elements.
- An evaluation on several open source projects found the technique was able to detect 189 warnings, with 171 warnings confirmed as real bugs by developers.
There exist many code review checklist blogs/articles in the net telling standard Java best practices/guidelines; And I have come up with a checklist accommodating best practices from some of the recent popular books (reference given) and guidelines from Oracle (on Security).
This document provides an introduction to binary exploitation. It outlines the course, which will cover basic stack overflows, shellcode injection, and exploit mitigation technologies. It explains how buffer overflows can be used to overwrite the return address and change the flow of execution. By injecting shellcode into the buffer and overwriting the return address to point to it, arbitrary code can be executed to gain unauthorized access. Modern defenses like ASLR and NX are discussed, as well as future topics like return-oriented programming and format string vulnerabilities. The overall goal is to understand software exploitation and how to identify vulnerabilities in programs.
The document discusses code review practices at the presenter's team. It covers basic concepts like broken window theory and technical debt. It then details the team's Git and Jira processes, coding standards using PHP CodeSniffer, and code review steps involving checking code, unit tests, and identifying inefficiencies and improvements. The concepts discussed will become obsolete when applications can self-improve by writing new code to meet business needs without human involvement.
This document discusses static code analysis and tools like SonarQube and Coverity. Static code analysis examines code without executing it to find bugs. Monitoring and fixing code quality issues improves application quality and delivery. SonarQube is an open source tool that manages code quality through analysis, issues detection, and metrics. Coverity also detects defects early through static analysis of various languages. Both tools help improve code quality.
Verification at scale: Fitting static code analysis into continuous integrationRogue Wave Software
Static code analysis (SCA) is a decades-proven software verification method that’s become essential for many development teams. With the growing adoption of DevOps processes and CI tools, it’s even more important that those familiar with and new to SCA understand how it fits into modern processes to maximize its benefits.
This talk describes three different ways of approaching static code analysis and explains the advantages and disadvantages of each, including test coverage, performance, and standards compliance. Starting with older server-based and desktop-based analysis, followed by the latest continuous static analysis for CI, you will walk away with an understanding of the different types of SCA and how to choose the best option that fits your team’s processes, environment, and release schedules.
Learning from other's mistakes: Data-driven code analysisAndreas Dewes
Static code analysis is an useful tool that can help to detect bugs early in the software development life cycle. I will explain the basics of static analysis and show the challenges we face when analyzing Python code. I will introduce a data-driven approach to code analysis that makes use of public code and example-based learning and show how it can be applied to analyzing Python code.
Slides and notes presented at Albany.pm on Thursday, January 23, 2014, covering static code analysis and an internal perl tool AutoLint, which automates Gimpel PC-Lint runs over large legacy C/C++ codebases. (The per-slide notes contain most of the spoken content.)
Static Analysis Techniques For Testing Application Security - Houston Tech FestDenim Group
Static Analysis of software refers to examining source code and other software artifacts without executing them. This presentation looks at how these techniques can be used to identify security defects in applications. Approaches examined will range from simple keyword search methods used to identify calls to banned functions through more sophisticated data flow analysis used to identify more complicated issues such as injection flaws. In addition, a demonstration will be given of two freely-available static analysis tools: FXCop and the beta version of Microsoft’s XSSDetect tool. Finally, some approaches will be presented on how organizations can start using static analysis tools as part of their development and quality assurance processes.
Studies show that for every 7 to 10 lines of code we write, we introduce one defect. Now often times we can spot these errors before they ever see the light of day, however that is not true in all cases. So what can we use to assist us in leveling the playing field? Well, we can take advantage of Static Code Analysis tools! In this talk, learn how you can incorporate the following tools into your development process: Checkstyle, PMD, FindBugs, and Lint.
This document summarizes information on anemia in heart failure patients. Some key points:
1. The prevalence of anemia in heart failure patients ranges from 20-30% for outpatients to 30-40% for inpatients, depending on the definition and study.
2. Anemia is associated with worse prognosis and increased risk of hospitalization and mortality in heart failure patients.
3. Potential treatment options for anemia in heart failure include blood transfusions, erythropoietin-stimulating proteins (ESPs), and iron therapy. However, clinical trials of ESPs like darbepoetin alfa have not shown clear benefits.
4. The FAIR-HF trial found
Unit Testing Concepts and Best PracticesDerek Smith
Unit testing involves writing code to test individual units or components of an application to ensure they perform as expected. The document discusses best practices for unit testing including writing atomic, consistent, self-descriptive tests with clear assertions. Tests should be separated by business module and type and not include conditional logic, loops, or exception handling. Production code should be isolated from test code. The goal of unit testing is to validate that code meets specifications and prevents regressions over time.
Unit testing involves testing individual units or components of code to ensure they work as intended. It focuses on testing small, isolated units of code to check functionality and edge cases. Benefits include faster debugging, development and regression testing. Guidelines for effective unit testing include keeping tests small, automated, independent and focused on the code's public API. Tests should cover a variety of inputs including boundaries and error conditions.
Après avoir fait ce talk à la conférence NSSpain, Simone Civetta va nous expliquer sur quelles métriques il est possible de se baser pour évaluer la qualité d’un code source. Cette question étant toujours sujette à débat, préparez vos arguments !
PVS-Studio advertisement - static analysis of C/C++ codePVS-Studio
This document advertises the PVS-Studio static analyzer. It describes how using PVS-Studio reduces the number of errors in code of C/C++/C++11 projects and costs on code testing, debugging and maintenance. A lot of examples of errors are cited found by the analyzer in various Open-Source projects. The document describes PVS-Studio at the time of version 4.38 on October 12-th, 2011, and therefore does not describe the capabilities of the tool in the next versions. To learn about new capabilities, visit the product's site http://www.viva64.com or search for an updated version of this article.
Having checked ReactOS's code I managed to fulfill three of my wishes at once. Firstly, I had wanted for a long time to write an article on a common project. It's not interesting to check the source code of projects like Chromium: its quality is too high and a lot of resources are spent to maintain it, which are unavailable to common projects. Secondly, it's a good example to demonstrate the necessity of static analysis in a large project, especially when it is developed by a diverse and distributed team. Thirdly, I've got a confirmation that PVS-Studio is becoming even better and more useful.
One of the biggest problems of software projects is that, while the practice of software development is commonly thought of as engineering, it is inherently a creative discipline; hence, many things about it are hard to measure. While simple yardsticks like test coverage and cyclomatic complexity are important for code quality, what other metrics can we apply to answer questions about our code? What coding conventions or development practices can we implement to make our code easier to measure? We'll take a tour through some processes and tools you can implement to begin improving code quality in your team or organization, and see what a difference it makes to long-term project maintainability. More importantly, we'll look at how we can move beyond today's tools to answer higher-level questions of code quality. Can 'good code' be quantified?
One of the biggest problems of software projects is that, while the practice of software development is commonly thought of as engineering, it is inherently a creative discipline; hence, many things about it are hard to measure. While simple yardsticks like test coverage and cyclomatic complexity are important for code quality, what other metrics can we apply to answer questions about our code? What coding conventions or development practices can we implement to make our code easier to measure? We'll take a tour through some processes and tools you can implement to begin improving code quality in your team or organization, and see what a difference it makes to long-term project maintainability. More importantly, we'll look at how we can move beyond today's tools to answer higher-level questions of code quality. Can 'good code' be quantified?
The document discusses improving code quality through effective code review processes. It outlines common coding mistakes like redundant code, long or deeply nested functions, large modules, poor comments, and hardcoding. It recommends following best practices like coding guidelines, centralized server communication, and the single responsibility principle. The document also discusses measuring and reducing code complexity, avoiding memory leaks, optimizing images, static code analysis, and profiling to improve code quality.
Data flow analysis is a type of static code analysis that examines how values are propagated through a program. It is more effective than pattern matching or regular static analysis at finding defects related to interactions between methods and classes that may be difficult to uncover through testing alone. Static analysis tools using data flow analysis can simulate execution paths to detect potential issues without requiring the code to be compiled and run. Developers are encouraged to use static testing tools to catch defects early in development, as prevention of bugs is more efficient than finding and fixing them later.
This document summarizes the analysis of the Qt 5.2.1 framework using the PVS-Studio static analysis tool. PVS-Studio detected 14 typos in Qt's code, including mistakes in variable names, missing comparisons, and identical subexpressions. It also found issues like loss of accuracy from integer division and an error related to operator priority. Overall, the author concludes Qt's code is high-quality but still contains ordinary typos that static analysis can help catch. Regular use of these tools could help prevent bugs early in development.
We continue checking Microsoft projects: analysis of PowerShellPVS-Studio
It has become a "good tradition" for Microsoft to make their products open-source: CoreFX, .Net Compiler Platform (Roslyn), Code Contracts, MSBuild, and other projects. For us, the developers of PVS-Studio analyzer, it's an opportunity to check well-known projects, tell people (including the project authors themselves) about the bugs we find, and additionally test our analyzer. Today we are going to talk about the errors found in another project by Microsoft, PowerShell.
This document provides tips and tricks for debugging Arbortext applications. It discusses challenges like debugging components with multiple interfaces and custom code. It recommends using messages like response() and eval to monitor state, and debugging tools like the Java console. It also suggests adding debug messages programmatically, using binary search, and getting a second set of eyes to help find bugs. Maintaining backups and good documentation are emphasized.
This document provides an overview of test-driven development (TDD) in Python. It describes the TDD process, which involves writing a test case that fails, then writing production code to pass that test, and refactoring the code. An example TDD cycle is demonstrated using the FizzBuzz problem. Unit testing in Python using the unittest framework is also explained. Benefits of TDD like improved code quality and safer refactoring are mentioned. Further reading on TDD and testing concepts from authors like Uncle Bob Martin and Kent Beck is recommended.
It is quite often that software developers have absolutely no clue about the cost of an error. It is very important that the error be found at the earliest possible stage.
Talos: Neutralizing Vulnerabilities with Security Workarounds for Rapid Respo...Zhen Huang
1) Security workarounds (SWRRs) aim to rapidly neutralize software vulnerabilities without introducing new bugs, by leveraging existing error handling mechanisms to disable vulnerable code.
2) An evaluation of SWRRs on 5 Linux applications found they could neutralize 53% of vulnerabilities unobtrusively, which is over 2 times as many as configuration workarounds alone.
3) The Talos tool automatically generates SWRRs by analyzing source code to identify error handling patterns and adding minimal instrumentation, requiring low developer effort. SWRRs introduce an average 1.3% runtime overhead.
The document discusses various ways to measure and improve code quality, including avoiding poor practices that can decrease code quality. It provides examples of real-world code with quality issues, such as unnecessary comments, exceptions handled poorly, and unclear naming. The document emphasizes writing code for readability and maintainability by future developers. It also promotes principles like keeping code simple, avoiding repetition, and separating concerns.
The document discusses bypassing endpoint detection and response (EDR) systems. It begins with an introduction and agenda, then provides background on the evolution of endpoint security technologies. It describes how EDRs and antiviruses work, including userland hooking techniques. The document outlines various 2022 EDR bypass techniques such as direct system calls, unhooking, and .NET evasion. It focuses on researching techniques to bypass AM-PPL (Antimalware Protected Process Light) and describes how to bypass it by abusing a 2018 vulnerability in Object Manager directories.
The document analyzes the source code of the Godot game engine using the PVS-Studio static code analyzer. It finds and discusses several types of errors identified by the analyzer, including duplicated comparisons, array overruns due to enum/array mismatches, incorrect data type checks, typos causing logic errors or infinite loops, and unsafe pointer usage. The analysis aims to both introduce readers to the Godot project and help its developers fix bugs and improve code quality.
The document discusses various techniques for debugging software bugs, including gathering relevant information, forming and testing hypotheses about the cause, and strategies like tracing execution, simplifying tests, questioning assumptions, and cleaning up unused code. It also provides a checklist for determining the root cause of bugs and ensuring debugging efforts are focused on the right location. The goal of debugging is to understand why bugs occur so they can be removed and prevent future bugs through improved testing, risk management, and learning from past issues.
What's the Difference Between Static Analysis and Compiler Warnings?Andrey Karpov
Visiting forums, you can often meet people who believe that compiler warnings are more than enough for controlling the basic types of errors in programs' code. I want to show you that it's a wrong belief.
Specialized static analysis tools and standard compiler warnings are both oriented towards improving the quality of the source code and minimizing potential bugs which are difficult to catch through debugging. One way or another, the compiler relies on static analysis of the source code during compilation to generate its warnings, but the quality of diagnostics and their scope of use vary greatly from one analysis method to another.
An important event has taken place in the PVS-Studio analyzer's life: support of C#-code analysis was added in the latest version. As one of its developers, I couldn't but try it on some project. Reading about scanning small and little-known projects is not much interesting of course, so it had to be something popular, and I picked MonoDevelop.
Здесь вы найдёте 60 вредных советов для программистов и пояснение, почему они вредные. Всё будет одновременно в шутку и серьёзно. Как бы глупо ни смотрелся вредный совет, он не выдуман, а подсмотрен в реальном мире программирования.
In this article, you're going to find 60 terrible coding tips — and explanations of why they are terrible. It's a fun and serious piece at the same time. No matter how terrible these tips look, they aren't fiction, they are real: we saw them all in the real programming world.
Ошибки, которые сложно заметить на code review, но которые находятся статичес...Andrey Karpov
Есть ошибки, которые легко прячутся от программистов на обзорах кода. Чаще всего они связаны с опечатками или недостаточным знанием тонких нюансах языка/библиотеки. Давайте посмотрим интересные примеры таких ошибок и как их можно выявить с помощью статического анализа. При этом анализаторы не конкурируют с обзорами кода или, например, юнит-тестами. Они отлично дополняют другие методологии борьбы с ошибками.
PVS-Studio analyzes source code and finds various errors and code quality issues across multiple languages and frameworks. The document highlights 20 examples of issues found, including uninitialized variables, unreachable code, incorrect operations, security flaws, and typos. PVS-Studio is able to find these issues using techniques such as data-flow analysis, method annotation analysis, symbolic execution, type inference, and pattern-based analysis to precisely evaluate the code and pinpoint potential bugs or code smells.
When should you start using PVS-Studio? What can PVS-Studio detect? Supported standards: MISRA, CWE, CERT, OWASP, AUTOSAR. What about analysis options? What about legacy code?
Двойное освобождение ресурсов. Недостижимый код. Некорректные операции сдвига. Неправильная работа с типами. Опечатки и copy-paste. Проблемы безопасности. Путаница с приоритетом операций.
Make Your and Other Programmer’s Life Easier with Static Analysis (Unreal Eng...Andrey Karpov
George Gribkov presented on how to introduce static analysis to make programmers' and QA engineers' lives easier. Static analysis automatically checks code for bugs without executing it. While initial attempts to analyze Unreal Engine 4 failed, monitoring compiler calls directly succeeded in finding over 1800 warnings. Epic Games now uses continuous static analysis to receive early warnings. The best practices are to start analysis early and regularly in development and CI/CD pipelines, and to gradually fix old warnings using suppression files to ratchet down reported issues over time. Static and dynamic analysis complement each other to thoroughly check for errors.
Best Bugs from Games: Fellow Programmers' MistakesAndrey Karpov
George Gribkov will present on errors found in the code of popular games like System Shock, Doom 3, and osu!. He will discuss how his tool searches for code errors, provide examples of bugs detected, and conclude his presentation. The examples will showcase issues like unused variables, incorrect increment variables in for loops, null pointer dereferences, and misunderstandings of operators like ??. Corrections will be proposed to address the bugs.
Does static analysis need machine learning?Andrey Karpov
This document discusses whether static analysis needs machine learning. It begins with an introduction to static analysis and outlines existing static analysis solutions like DeepCode, Infer, SapFix, Embold, Source{d}, Clever-Commit, and CodeGuru. It then addresses problems with learning manually or from real large code bases, like outdated code and lack of documentation. Finally, it discusses promising approaches like analyzing code style, collecting additional metrics, and best practices for specific frameworks.
Typical errors in code on the example of C++, C#, and JavaAndrey Karpov
Objectives of this webinar
How we detected error patterns
Patterns themselves and how to avoid them:
3.1 Copy-paste and last line effect
3.2 if (A) {...} else if (A)
3.3 Errors in checks
3.4 Array index out of bounds
3.5 Operator precedence
3.6 Typos that are hard to spot
How to use static analysis properly
Conclusion
Q&A
How to Fix Hundreds of Bugs in Legacy Code and Not Die (Unreal Engine 4)Andrey Karpov
How to fight bugs in legacy code?
Should you do it at all?
What to do if there are hundreds or even thousands of errors?(that’s usually the case)
How to avoid spending a plethora of man-hours on this?
And still, how did you work with Unreal Engine?
C++ Code as Seen by a Hypercritical ReviewerAndrey Karpov
We all do code reviews. Who doesn't admit this – does it twice as often. C++ code reviewers look like a sapper. .. except that they can make a mistake more than once. But sometimes the consequences are painful . Brave code review world.
The Use of Static Code Analysis When Teaching or Developing Open-Source SoftwareAndrey Karpov
The document discusses using static code analysis when teaching or developing open-source software. It outlines how static analysis can help instructors check student homework and projects more efficiently, and help students learn about error patterns. When using static analysis for open-source projects, it recommends integrating it into developers' workflows locally and via continuous integration systems. Regular use is key to maximizing its benefits for finding and fixing bugs.
Are С and C++ Alive? Even More, IBM RPG Is! C and C++ Are Not Just for Old Systems. Are С and C++ Alive? Summary for C, C++. Embedded: C and С++ Are on the Rise.
Zero, one, two, Freddy's coming for youAndrey Karpov
This post continues the series of articles, which can well be called "horrors for developers". This time it will also touch upon a typical pattern of typos related to the usage of numbers 0, 1, 2. The language you're writing in doesn't really matter: it can be C, C++, C#, or Java. If you're using constants 0, 1, 2 or variables' names contain these numbers, most likely, Freddy will come to visit you at night. Go on, read and don't say we didn't warn you.
PVS-Studio Is Now in Chocolatey: Checking Chocolatey under Azure DevOpsAndrey Karpov
The document discusses integrating the PVS-Studio static code analyzer with Azure DevOps and Chocolatey. It provides steps to configure a build pipeline in Azure DevOps to install PVS-Studio using Chocolatey, run analysis on a project, and publish the results. The analysis found several potential bugs in the Chocolatey code including logical errors, redundant checks, and null reference issues. Integrating PVS-Studio with these tools helps improve code quality.
PVS-Studio Static Analyzer as a Tool for Protection against Zero-Day Vulnerab...Andrey Karpov
A Zero-day (0-day) vulnerability is a computer-software vulnerability introduced during the development process and not yet discovered by the developers. Zero-day vulnerabilities can be exploited by hackers, thus affecting the company's reputation. Developers should seek to minimize the number of defects leading to such vulnerabilities. PVS-Studio, a static code analyzer for C, C++, C#, and Java code, is one of the tools capable of detecting security issues.
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How I solved production issues with OpenTelemetryCees Bos
Ensuring the reliability of your Java applications is critical in today's fast-paced world. But how do you identify and fix production issues before they get worse? With cloud-native applications, it can be even more difficult because you can't log into the system to get some of the data you need. The answer lies in observability - and in particular, OpenTelemetry.
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Troubleshooting JVM Outages – 3 Fortune 500 case studiesTier1 app
In this session we’ll explore three significant outages at major enterprises, analyzing thread dumps, heap dumps, and GC logs that were captured at the time of outage. You’ll gain actionable insights and techniques to address CPU spikes, OutOfMemory Errors, and application unresponsiveness, all while enhancing your problem-solving abilities under expert guidance.
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2. What is «static code analysis»?
• It is a technique that allows, at the same time with unit-tests, dynamic code
analysis, code review and others, to increase code quality, increase its
reliability and decrease the development time.
• It should be noted that static code analysis is not a universal panacea and is
maximally effective in conjunction with other methods of code testing.
3. Who needs static code analysis?
• Any medium-sized and large software development company – to increase
code reliability and decrease its price,
• Any small company and individual developers – in a lesser extent – to drink
coffee instead of searching and fixing annoying bugs,
• Anyone, who supports any old code,
• Specialists for specific tasks (for instance, Sparce code analyzer for Linux
kernel hackers).
4. Static code analysis advantages
• Allows to find bugs on early stages (the earlier the bug was spotted, the
cheaper it is to be fixed),
• High analysis speed,
• Does not require to run the application, only an access to source code and
(not always) – to preprocessed files,
• Allows to locate bugs in code that is rarely executed (exception handlers, for
instance).
5. Static code analysis disadvantages
• Possibility of false positive alarm on correct code,
• Correct positive alarms on old code, which works correctly and which should
better not be bothered, may be nauseous.
• Comparatively small class of bugs detected due to the exponential difficulty
of “honest” bug search.
• Does not detects logical errors (this is a drawback of almost all automatic
testing tools in contrast to code review and manually written unit tests).
6. PVS-Studio
• One of static code analysis tools for C and C++ languages (including C++CX,
C++0x and C++11),
• Developers – ООО «Program Verification Systems».
• Site: http://www.viva64.com/
• From so on, main advantages of this tool will be listed.
7. PVS-Studio: ease of use
• Allows integration into Microsoft Visual Studio (except for Express version –
it lacks extension mechanisms),
• Includes PVS-Studio Standalone that does not require IDE at all,
• Works quickly and “out-of-the-box”, does not require dedicated database
servers and personnel training.
• Can be integrated into the build system,
• Fully-functional trial version.
8. PVS-Studio: features
• Incremental analysis allows to find bugs in new code after every build,
• Message suppression allows to concentrate on a newly written code by
hiding all the warning messages on the old code (of course, they can be
reviewed later),
• Special feature – search for bugs that shows up on porting 32-bit application
into 64-bit ones.
9. PVS-Studio: additional features
• Quick tech support,
• Users may ask for a features in a future releases. Our tool is expanding, and
we try to take into account every request,
• All errors are properly documented and there are a lot of examples (small
fraction of them will be listed on the next slides).
10. Examples of errors found in
real-life applications
Error #1
while (node != NULL) {
if ((node->hashCode == code) &&
(node->entry.key == key)) {
return true;
}
node = node->next;
} while (node != NULL);
It seems like do / while cycles was mixed
up in a weird way here. Of course,
second ‘while’ operator should never
become an endless cycle, but is there
actually ‘while’ and not ‘do’ cycle
required?
11. Examples of errors found in
real-life applications
Error #2
int main(int argc, char** argv) {
....
if (getIsInteractiveMode())
//p->writePepSHTML();
//p->printResult();
// regression test?
if (testType!=NO_TEST) {
....
}
}
Even comments can sometimes harm the
program, especially in the wrong place. In
this piece of code second ‘if’ operator will
only be evaluated if condition in first ‘if’ is
true, but code formatting says that the
opposite was intended. By the way, this
error was found in unit tests.
12. Examples of errors found in
real-life applications
Error #3
HRESULT
SHEOW_LoadOpenWithItems(....)
{
....
if (_ILIsDesktop(pidl) || _ILIsMyDocuments(pidl)
|| _ILIsControlPanel(pidl) || _ILIsNetHood(pidl)
|| _ILIsBitBucket(pidl) || _ILIsDrive(pidl)
|| _ILIsCPanelStruct(pidl) || _ILIsFolder(pidl)
|| _ILIsControlPanel(pidl))
{
TRACE("pidl is a foldern");
SHFree((void*)pidl);
return E_FAIL;
}
....
}
For everyone who thinks that every
problem that was found by static code
analyzer can also be found by code
review. Good luck in figuring out what’s
wrong here. And don’t forget that real
code is much, much bigger than this
fragment.
13. Examples of errors found in
real-life applications
Error #3
HRESULT
SHEOW_LoadOpenWithItems(....)
{
....
if (_ILIsDesktop(pidl) || _ILIsMyDocuments(pidl)
|| _ILIsControlPanel(pidl) || _ILIsNetHood(pidl)
|| _ILIsBitBucket(pidl) || _ILIsDrive(pidl)
|| _ILIsCPanelStruct(pidl) || _ILIsFolder(pidl)
|| _ILIsControlPanel(pidl))
{
TRACE("pidl is a foldern");
SHFree((void*)pidl);
return E_FAIL;
}
....
}
Here it is. A repeated fragment in a
logical expression. At least one of this
repeated sentences is redundant. More
likely scenario: one of this sentences is
incorrect, and programmer should have
meant something else.
14. Examples of errors found in
real-life applications
Error #4
Style & w1Style =
_pUserLang->_styleArray.getStyler(STYLE_WORD1_INDEX);
styleUpdate(w1Style, _pFgColour[0], _pBgColour[0],
IDC_KEYWORD1_FONT_COMBO, IDC_KEYWORD1_FONTSIZE_COMBO,
IDC_KEYWORD1_BOLD_CHECK, IDC_KEYWORD1_ITALIC_CHECK,
IDC_KEYWORD1_UNDERLINE_CHECK);
Style & w2Style =
_pUserLang->_styleArray.getStyler(STYLE_WORD2_INDEX);
styleUpdate(w2Style, _pFgColour[1], _pBgColour[1],
IDC_KEYWORD2_FONT_COMBO, IDC_KEYWORD2_FONTSIZE_COMBO,
IDC_KEYWORD2_BOLD_CHECK, IDC_KEYWORD2_ITALIC_CHECK,
IDC_KEYWORD2_UNDERLINE_CHECK);
Style & w3Style =
_pUserLang->_styleArray.getStyler(STYLE_WORD3_INDEX);
styleUpdate(w3Style, _pFgColour[2], _pBgColour[2],
IDC_KEYWORD3_FONT_COMBO, IDC_KEYWORD3_FONTSIZE_COMBO,
IDC_KEYWORD3_BOLD_CHECK, IDC_KEYWORD3_BOLD_CHECK,
IDC_KEYWORD3_UNDERLINE_CHECK);
Style & w4Style =
_pUserLang->_styleArray.getStyler(STYLE_WORD4_INDEX);
styleUpdate(w4Style, _pFgColour[3], _pBgColour[3],
IDC_KEYWORD4_FONT_COMBO, IDC_KEYWORD4_FONTSIZE_COMBO,
IDC_KEYWORD4_BOLD_CHECK, IDC_KEYWORD4_ITALIC_CHECK,
IDC_KEYWORD4_UNDERLINE_CHECK);
Still not impressed? Well, here comes
another example.
15. Examples of errors found in
real-life applications
Error #4
Nice example of code produced by
copy-paste technique featuring
programmer who forgot to fix one
word. This error is definitely hard to
detect using only code review.
However, if you enjoyed searching for
errors, we have a quiz for you.
Style & w1Style =
_pUserLang->_styleArray.getStyler(STYLE_WORD1_INDEX);
styleUpdate(w1Style, _pFgColour[0], _pBgColour[0],
IDC_KEYWORD1_FONT_COMBO, IDC_KEYWORD1_FONTSIZE_COMBO,
IDC_KEYWORD1_BOLD_CHECK, IDC_KEYWORD1_ITALIC_CHECK,
IDC_KEYWORD1_UNDERLINE_CHECK);
Style & w2Style =
_pUserLang->_styleArray.getStyler(STYLE_WORD2_INDEX);
styleUpdate(w2Style, _pFgColour[1], _pBgColour[1],
IDC_KEYWORD2_FONT_COMBO, IDC_KEYWORD2_FONTSIZE_COMBO,
IDC_KEYWORD2_BOLD_CHECK, IDC_KEYWORD2_ITALIC_CHECK,
IDC_KEYWORD2_UNDERLINE_CHECK);
Style & w3Style =
_pUserLang->_styleArray.getStyler(STYLE_WORD3_INDEX);
styleUpdate(w3Style, _pFgColour[2], _pBgColour[2],
IDC_KEYWORD3_FONT_COMBO, IDC_KEYWORD3_FONTSIZE_COMBO,
IDC_KEYWORD3_BOLD_CHECK, IDC_KEYWORD3_BOLD_CHECK,
IDC_KEYWORD3_UNDERLINE_CHECK);
Style & w4Style =
_pUserLang->_styleArray.getStyler(STYLE_WORD4_INDEX);
styleUpdate(w4Style, _pFgColour[3], _pBgColour[3],
IDC_KEYWORD4_FONT_COMBO, IDC_KEYWORD4_FONTSIZE_COMBO,
IDC_KEYWORD4_BOLD_CHECK, IDC_KEYWORD4_ITALIC_CHECK,
IDC_KEYWORD4_UNDERLINE_CHECK);
16. Examples of errors found in
real-life applications
Error #5
void ListJob::doStart()
{
Q_D( ListJob );
switch ( d->option ) {
break;
case IncludeUnsubscribed:
d->command = "LIST";
break;
case IncludeFolderRoleFlags:
d->command = "XLIST";
break;
case NoOption:
default:
d->command = "LSUB";
}
....
}
One single ‘break’ in unusual place may
alter the whole ‘switch’ statement
behavior. Or maybe it was intentional,
wasn’t it?
17. Conclusion
• All the errors listed in this presentation was found in open-source projects. It
proves that even professional programmers tend to make errors.
• It is worth to remind that it is better to use the whole bunch of tools, not only static
code analysis or only unit tests, and to give enough attention to refactoring and
code quality. We are almost certain that this will pay for itself. Analyzer may find a
misprint, but would never find a wrong algorithm! Unit tests may contain errors
too, and human attention would hardly find a misprint in heaps of duplicate code.
• Good luck with development!
18. Additional links:
• PVS-Studio: http://www.viva64.com/en/pvs-studio/
• Updatable List of Open-Source Projects Checked with PVS-Studio:
http://www.viva64.com/en/a/0084/
• Blog: http://www.viva64.com/en/b/
• Twitter: https://twitter.com/Code_Analysis