Statistical Debugging

Overview

This article covers the “Statistical Debugging” chapter from The Debugging Book. In this article we will summarize the chapter and discuss how its content could be utilized within the context of the implementation and testing of the Chasten and Cellveyor tools.

Summary

Statistical Debugging is a method used to troubleshoot programs that exhibit both passing and failing behavior depending on the inputs they receive. The primary objective is to establish a connection between program failure(s) and specific part(s) of the code, whether it’s a single line or multiple lines. The process starts with a collector, a class responsible for gathering information about each line of the program’s execution.

The provided example introduces a simple collector class from the “Debugging Book”, named Collector, which records events during execution. This class can be extended and customized for specific needs.

class Collector(Tracer):
    """A class to record events during execution."""

    def collect(self, frame: FrameType, event: str, arg: Any) -> None:
        """Collecting function. To be overridden in subclasses."""
        pass

    def events(self) -> Set:
        """Return a collection of events. To be overridden in subclasses."""
        return set()

    def traceit(self, frame: FrameType, event: str, arg: Any) -> None:
        self.collect(frame, event, arg)

After running the program multiple times, the lines of code are then ranked by suspiciousness. This metric determines the likelihood of a line causing a program failure. The ranking is established by analyzing which parts of the code were ran during failing runs and comparing them to successful runs.

A key part of the process is knowing what is being run through tracking the Code Coverage. The below subclass from the “Debugging Book” is used to track which lines are being run an returns a set of tuples with the function names and the lines ran. This allows the collector keep track of what is being run in a formatted way to be used later.

class CoverageCollector(Collector, StackInspector):
    """A class to record covered locations during execution."""

    def __init__(self) -> None:
        """Constructor."""
        super().__init__()
        self._coverage: Coverage = set()

    def collect(self, frame: FrameType, event: str, arg: Any) -> None:
        """
        Save coverage for an observed event.
        """
        name = frame.f_code.co_name
        function = self.search_func(name, frame)

        if function is None:
            function = self.create_function(frame)

        location = (function, frame.f_lineno)
        self._coverage.add(location)
class CoverageCollector(CoverageCollector):
    def events(self) -> Set[Tuple[str, int]]:
        """
        Return the set of locations covered.
        Each location comes as a pair (`function_name`, `lineno`).
        """
        return {(func.__name__, lineno) for func, lineno in self._coverage}

The ultimate goal is to find a connection between failing and passing runs and specific parts of the code. This involves splitting the collected information based on whether the outcome was a PASS or FAIL. The following code snippet exemplifies this process:

class StatisticalDebugger(StatisticalDebugger):
    def collect(self, outcome: str, *args: Any, **kwargs: Any) -> Collector:
        """Return a collector for the given outcome. 
        Additional args are passed to the collector."""
        collector = self.collector_class(*args, **kwargs)
        collector.add_items_to_ignore([self.__class__])
        return self.add_collector(outcome, collector)

    def add_collector(self, outcome: str, collector: Collector) -> Collector:
        if outcome not in self.collectors:
            self.collectors[outcome] = []
        self.collectors[outcome].append(collector)
        return collector

After being grouped the results are then ready to be analyzed and ranked. Making this a multi-step process that can be complicated and requires a significant amount of computation to achieve. Despite that, statistical debugging provides crucial insights into how a program fails over time, aiding in the identification of root causes. The ability to analyze a codebase being especially valuable, especially in enhancing the debugging process for large projects where efficiency is essential.

Reflection

Statistical debugging is a powerful method for troubleshooting programs with failures. Its key objective is to establish a connection between program failures and specific parts of the code. The process involves collecting data during program execution, ranking lines of code by failure possibility, and analyzing the differences between passing and failing runs.

The provided code snippets illustrate a basic framework for statistical debugging, using a Collector class to gather information during execution. This class can be extended and customized to suit specific debugging needs.

The concept of code coverage plays a crucial role in statistical debugging. The CoverageCollector subclass is introduced to track which lines of code are executed and returns a formatted set of tuples with function names and corresponding line numbers. This coverage information is essential for understanding what parts of the code are contributing to program failures.

The StatisticalDebugger class is then presented as a way to organize and analyze the collected data. It uses different collectors for passing and failing outcomes and facilitates the grouping of results for further analysis. This multi-step process involves significant computation but provides insights into the behavior of the program over multiple runs.

In summary, statistical debugging is a complex approach that requires careful instrumentation of code and analysis of collected data. Despite its implementation complexity, it offers valuable benefits, especially in large projects where efficiently identifying and resolving bugs is crucial. The ability to analyze a codebase over multiple runs provides developers with a deeper understanding of the program’s behavior, ultimately aiding in the identification of root causes for intermittent failures.

Action Items

Statistical debugging is like having a special tool to find and fix mistakes in big and complicated computer programs. Imagine if you had a really huge puzzle, and instead of looking at each piece one by one, you use a smart method to see which pieces are most likely to be part of a problem. Statistical debugging does something similar for computer programs. It helps developers understand how the program behaves and where mistakes might be hiding. This way, they can find and fix the issues faster, especially when dealing with large and complex code. Our development teams could incorporate this technique for debugging, but ultimately would have to decide as a team if implementing this type of technique would be necessary for our tools chasten and cellveyor specifically. It might prove to be more beneficial in the future though, as our code becomes more and more complex.

Return to Blog Post Listing