Algorithmic program debugging

Abstract: The thesis lays a theoretical framework for program debugging, with the goal of partly mechanizing this activity. In particular, we formalize and develop algorithmic solutions to the following two questions: (1) How do we identify a bug in a program that behaves incorrectly? (2) How do we fix a bug, once one is identified? We develop interactive diagnosis algorithms that identify a bug in a program that behaves incorrectly, and implement them in Prolog for the diagnosis of Prolog programs. Their performance suggests that they can be the backbone of debugging aids that go far beyond what is offered by current programming environments. We develop an inductive inference algorithm that synthesizes logic programs from examples of their behavior. The algorithm incorporates the diagnosis algorithms as a component. It is incremental, and progresses by debugging a program with respect to the examples. The Model Inference System is a Prolog implementation of the algorithm. Its range of applications and efficiency is comparable to existing systems for program synthesis from examples and grammatical inference. We develop an algorithm that can fix a bug that has been identified, and integrate it with the diagnosis algorithms to form an interactive debugging system. By restricting the class of bugs we attempt to correct, the system can debug programs that are too complex for the Model Inference System to synthesize.

Abstract: Debugging is notoriously difficult and extremely time consuming. Researchers have therefore invested a considerable amount of effort in developing automated techniques and tools for supporting various debugging tasks. Although potentially useful, most of these techniques have yet to demonstrate their practical effectiveness. One common limitation of existing approaches, for instance, is their reliance on a set of strong assumptions on how developers behave when debugging (e.g., the fact that examining a faulty statement in isolation is enough for a developer to understand and fix the corresponding bug). In more general terms, most existing techniques just focus on selecting subsets of potentially faulty statements and ranking them according to some criterion. By doing so, they ignore the fact that understanding the root cause of a failure typically involves complex activities, such as navigating program dependencies and rerunning the program with different inputs. The overall goal of this research is to investigate how developers use and benefit from automated debugging tools through a set of human studies. As a first step in this direction, we perform a preliminary study on a set of developers by providing them with an automated debugging tool and two tasks to be performed with and without the tool. Our results provide initial evidence that several assumptions made by automated debugging techniques do not hold in practice. Through an analysis of the results, we also provide insights on potential directions for future work in the area of automated debugging.

Abstract: Consider the execution of a failing program as a sequence of program states. Each state induces the following state, up to the failure. Which variables and values of a program state are relevant for the failure? We show how the Delta Debugging algorithm isolates the relevant variables and values by systematically narrowing the state difference between a passing run and a failing run---by assessing the outcome of altered executions to determine wether a change in the program state makes a difference in the test outcome. Applying Delta Debugging to multiple states of the program automatically reveals the cause-effect chain of the failure---that is, the variables and values that caused the failure.In a case study, our prototype implementation successfully isolated the cause-effect chain for a failure of the GNU C compiler: "Initially, the C program to be compiled contained an addition of 1.0; this caused an addition operator in the intermediate RTL representation; this caused a cycle in the RTL tree---and this caused the compiler to crash."

Abstract: This book is proof that debugging has graduated from a black art to a systematic discipline It demystifies one of the toughest aspects of software programming, showing clearly how to discover what caused software failures, and fix them with minimal muss and fuss The fully updated second edition includes 100+ pages of new material, including new chapters on Verifying Code, Predicting Erors, and Preventing Errors Cutting-edge tools such as FindBUGS and AGITAR are explained, techniques from integrated environments like Jazznet are highlighted, and all-new demos with ESC/Java and Spec#, Eclipse and Mozilla are included This complete and pragmatic overview of debugging is authored by Andreas Zeller, the talented researcher who developed the GNU Data Display Debugger(DDD), a tool that over 250,000 professionals use to visualize the data structures of programs while they are running Unlike other books on debugging, Zeller's text is product agnostic, appropriate for all programming languages and skill levels The book explains best practices ranging from systematically tracking error reports, to observing symptoms, reproducing errors, and correcting defects It covers a wide range of tools and techniques from hands-on observation to fully automated diagnoses, and also explores the author's innovative techniques for isolating minimal input to reproduce an error and for tracking cause and effect through a program It even includes instructions on how to create automated debugging tools The text includes exercises and extensive references for further study, and a companion website with source code for all examples and additional debugging resources is available *The new edition of this award-winning productivity-booster is for any developer who has ever been frustrated by elusive bugs *Brand new chapters demonstrate cutting-edge debugging techniques and tools, enabling readers to put the latest time-saving developments to work for them *Learn by doing New exercises and detailed examples focus on emerging tools, languages and environments, including AGITAR, FindBUGS, Python and Eclipse