Execution mining

Lefebvre, Geoffrey; Cully, Brendan; Head, Christopher; Spear, Mark; Hutchinson, Norman C.; Feeley, Michael J.; Warfield, Andrew

doi:10.1145/2151024.2151044

Cited by 14 publications

(9 citation statements)

References 33 publications

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“…Tralfamadore [8] is a system for analysis of large execution traces. It can display the most frequently-occurring values of a specific function parameter.…”

Section: Dynamic Analysismentioning

confidence: 99%

“…Furthermore, none of the mentioned approaches provide concrete, literate string representations of arguments, return values and states from runtime: at best, they provide code examples that use the API. While there exist dynamic analysis approaches collecting run-time values of variables [7,8], they are not oriented toward textual documentation generation.…”

Section: Introductionmentioning

confidence: 99%

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Source Code Documentation Generation Using Program Execution

Sulír¹,

Porubãn²

2017

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Automated source code documentation approaches often describe methods in abstract terms, using the words contained in the static source code or code excerpts from repositories. In this paper, we describe DynamiDoc: a simple automated documentation generator based on dynamic analysis. Our representation-based approach traces the program being executed and records string representations of concrete argument values, a return value and a target object state before and after each method execution. Then, for each method, it generates documentation sentences with examples, such as "When called on [3, 1.2] with element = 3, the object changed to [1.2]". Advantages and shortcomings of the approach are listed. We also found out that the generated sentences are substantially shorter than the methods they describe. According to our small-scale study, the majority of objects in the generated documentation have their string representations overridden, which further confirms the potential usefulness of our approach. Finally, we propose an alternative, variable-based approach that describes the values of individual member variables, rather than the state of an object as a whole.

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“…Tralfamadore [8] is a system for analysis of large execution traces. It can display the most frequently-occurring values of a specific function parameter.…”

Section: Dynamic Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Source Code Documentation Generation Using Program Execution

Sulír¹,

Porubãn²

2017

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“…Omniscient debuggers work by logging the state of the program being debugged after every instruction, and then reconstructing the state from the log on demand. Some examples of omniscient debuggers include ODB [13], Amber (also known as Chronicle) [6], Tralfamadore [14], and TOD [15]. In contrast, replay debuggers work by logging the results of system calls the program makes (as well as other sources of nondeterminism) and making intermediate checkpoints, so that the debugger can reconstruct a requested program state by starting at a checkpoint and replaying the program with the logged system calls.…”

Section: A Time-travel Debuggersmentioning

confidence: 99%

Expositor: Scriptable time-travel debugging with first-class traces

Phang

Foster

Hicks

2013

2013 35th International Conference on Software Engineering (ICSE)

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Abstract-We present EXPOSITOR, a new debugging environment that combines scripting and time-travel debugging to allow programmers to automate complex debugging tasks. The fundamental abstraction provided by EXPOSITOR is the execution trace, which is a time-indexed sequence of program state snapshots. Programmers can manipulate traces as if they were simple lists with operations such as map and filter. Under the hood, EXPOSITOR efficiently implements traces as lazy, sparse interval trees whose contents are materialized on demand. EXPOSITOR also provides a novel data structure, the edit hash array mapped trie, which is a lazy implementation of sets, maps, multisets, and multimaps that enables programmers to maximize the efficiency of their debugging scripts. We have used EXPOSITOR to debug a stack overflow and to unravel a subtle data race in Firefox. We believe that EXPOSITOR represents an important step forward in improving the technology for diagnosing complex, hard-to-understand bugs.

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“…Senseo [10] displays information such as lists of callers, callees and dynamic argument types. Tralfamadore [6] is limited to argument and return values, IDE sparklines [1] to numeric variable types.…”

Section: Introductionmentioning

confidence: 99%

Augmenting source code lines with sample variable values

Sulír

Porubãn

2018

Proceedings of the 26th Conference on Program Comprehension

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Source code is inherently abstract, which makes it difficult to understand. Activities such as debugging can reveal concrete runtime details, including the values of variables. However, they require that a developer explicitly requests these data for a specific execution moment. We present a simple approach, RuntimeSamp, which collects sample variable values during normal executions of a program by a programmer. These values are then displayed in an ambient way at the end of each line in the source code editor. We discuss questions which should be answered for this approach to be usable in practice, such as how to efficiently record the values and when to display them. We provide partial answers to these questions and suggest future research directions. CCS CONCEPTS• Software and its engineering → Integrated and visual development environments; Software maintenance tools; KEYWORDS integrated development environment (IDE), source code augmentation, examples, variables, dynamic analysis ACM Reference Format:

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Execution mining

Cited by 14 publications

References 33 publications

Source Code Documentation Generation Using Program Execution

Source Code Documentation Generation Using Program Execution

Expositor: Scriptable time-travel debugging with first-class traces

Augmenting source code lines with sample variable values

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