A Systematic Survey of Program Comprehension through Dynamic Analysis
By B. Cornelissen, A. Zaidman, Arie van Deursen, Leon Moonen, Rainer Koschke. IEEE Transactions on Software Engineering, 2008
458 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Information Systems, Software
View Paper: https://www.semanticscholar.org/paper/42f6fc803b52c395a624284f3a7390e7799d7b0e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Christian Kästner
- Sven Apel
- Collin McMillan
- Iulian Neamtiu
- Denys Poshyvanyk
- Arie van Deursen
- Don S. Batory
- Alexander LeClair
In Information Systems
- Christian Kästner
- Sven Apel
- Gunter Saake
- Denys Poshyvanyk
- Collin McMillan
- Alexander LeClair
- Don S. Batory
- Sakib Haque
In Software
- Christian Kästner
- Sven Apel
- Gunter Saake
- Denys Poshyvanyk
- Tanzirul Azim
- Alexander LeClair
- Collin McMillan
- Arie van Deursen
Citations in the corpus
- Improved Code Summarization via a Graph Neural Network (2020, background): “ese summaries have several advantages when put into documentation such as decreased time to understand code [17], improved code comprehension [11, 54], and to making code more searchable [22].”
- Feature-Oriented Software Product Lines (2013, background): “For yet other concerns of the system that are currently not configurable, no tracing information may be present in the source code; in such cases, tracing links can be extracted manually or with semi-automatic feature-location tools (Biggerstaff et al. 1993; Robillard and Murphy 2002; Poshyvanyk et ”
- A Controlled Experiment for Program Comprehension through Trace Visualization (2011, methodology): “Last, most existing approaches involve traditional visualizations, i.e., they rely on UML, graph, or tree notations, to which most software engineers are presumably accustomed [9].”
- Targeted and depth-first exploration for systematic testing of android apps (2013, background): “However, dynamic analysis critically hinges on the availability of test cases that can ensure good coverage, i.e., drive program execution through a significant set of representative program states [36, 37].”
- Feature location in source code: a taxonomy and survey (2013, background): “Another systematic survey of feature location approaches utilizing execution information can be found in a comprehensive survey of approaches to program comprehension via dynamic analysis [31].”