Dynamic Analysis Techniques for the Reconstruction of Architectural Views

Cornelissen, B.

doi:10.1109/wcre.2007.23

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…Program comprehension is gained by performing static analysis of a program code and dynamic analysis of its execution [Ric02], [RD99], [Cor07]. Several tools and techniques offer support for these activities.…”

Section: Queries To Support Dynamic Analysismentioning

confidence: 99%

Time-Traveling Debugging Queries: Faster Program Exploration

Willembrinck

Costiou

Etien

et al. 2021

2021 IEEE 21st International Conference on Software Quality, Reliability and Security (QRS)

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Efficiently debugging a program requires program comprehension. To acquire it, developers explore the program execution, a task often performed using interactive debuggers. Unfortunately, exploring a program execution through standard interactive debuggers is a tedious and costly task. In this paper, we propose Time-Traveling Queries (TTQs) to ease program exploration. TTQs is a mechanism that automatically explores program executions to collect execution data. This data is used to time-travel through execution states, facilitating the exploration of program executions. We built a set of key TTQs based on typical questions developers ask when trying to understand programs. We conducted a user study with 34 participants to evaluate the impact of our queries on program comprehension activities. Results show that, compared to traditional debugging tools, TTQs significantly improve developers' precision, while reducing required time and efforts when performing program comprehension tasks.

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Section: Queries To Support Dynamic Analysismentioning

confidence: 99%

Time-Traveling Debugging Queries: Faster Program Exploration

Willembrinck

Costiou

Etien

et al. 2021

2021 IEEE 21st International Conference on Software Quality, Reliability and Security (QRS)

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“…This quality function only considers the call frequency metric, which is insufficient without including metrics like our call weight and call distance. In [28], the authors presented an approach to extract an architecture view of object-oriented software using the analysis of execution traces. This research contribution lacks details on how such a process can be automated, and only the visualization aspect is discussed.…”

Section: Related Workmentioning

confidence: 99%

Identifying software components from object-oriented APIs based on dynamic analysis

Shatnawi

Shatnawi²,

Saied

et al. 2018

Proceedings of the 26th Conference on Program Comprehension

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The reuse at the component level is generally more effective than the one at the object-oriented class level. This is due to the granularity level where components expose their functionalities at an abstract level compared to the fine-grained object-oriented classes. Moreover, components clearly define their dependencies through their provided and required interfaces in an explicit way that facilitates the understanding of how to reuse these components. Therefore, several component identification approaches have been proposed to identify components based on the analysis object-oriented software applications. Nevertheless, most of the existing component identification approaches did not consider co-usage dependencies between API classes to identify classes/methods that can be reused to implement a specific scenario. In this paper, we propose an approach to identify reusable software components in object-oriented APIs, based on the interactions between client applications and the targeted API. As we are dealing with actual clients using the API, dynamic analysis allows to better capture the instances of API usage. Approaches using static analysis are usually limited by the difficulty of handling dynamic features such as polymorphism and class loading. We evaluate our approach by applying it to three Java APIs with eight client applications from the DaCapo benchmark. DaCapo provides a set of pre-defined usage scenarios. The results show that our component identification approach has a very high precision.

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“…• Optimizing the generated code during compilation [1] • Automatically pinpointing (potential) errors and as such reducing debugging time [119] • Automatically detecting vulnerabilities and security threats [92] • Supporting the understanding and comprehension of large and complex programs [20] • Reverse engineering legacy software systems [87] Several types of program analysis techniques are used in practice as a part of existing software development tools and compilers, which are tailored for different execution platforms and programming languages. There are mainly two approaches for program analysis; static analysis and dynamic analysis.…”

Section: Source Code and Run-time Analysismentioning

confidence: 99%

“…allocated resources) can be specified (lines 10-12) as a preparation for recovery. Post-recovery initialization (lines [14][15][16][17][18][19][20] by default includes maintaining the connection with the CM and the RM (line 15), obtaining the check-pointed state variables (line 17) and start processing incoming messages from other RUs (line 19). Additional RU-specific initialization actions can also be specified here.…”

Section: Application Of Floramentioning

confidence: 99%

Architecting fault-tolerant software systems

Sözer¹

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Dynamic Analysis Techniques for the Reconstruction of Architectural Views

Cited by 4 publications

References 16 publications

Time-Traveling Debugging Queries: Faster Program Exploration

Time-Traveling Debugging Queries: Faster Program Exploration

Identifying software components from object-oriented APIs based on dynamic analysis

Architecting fault-tolerant software systems

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