Feature-level phase detection for execution trace using object cache

Watanabe, Yui; Ishio, Takashi; Inoue, Katsuro

doi:10.1145/1401827.1401830

Cited by 24 publications

(22 citation statements)

References 32 publications

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“…Similar to other works (e.g., [5,6,12]) in the domain of trace analysis for program comprehension we use the following definition of execution phases: "a program [execution] phase is a portion of the program that exhibits common behavior at a level the programmer would recognize" [5]. In such context, we want to be able to take an execution trace (generated from exercising one or more particular features) and identify a set of high-level tasks (i.e.…”

Section: What Is An Execution Phase?supporting

confidence: 83%

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A Novel Approach Based on Gestalt Psychology for Abstracting the Content of Large Execution Traces for Program Comprehension

Pirzadeh

Hamou-Lhadj

2011

2011 16th IEEE International Conference on Engineering of Complex Computer Systems

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The analysis of execution traces can reveal important information about the behavioral aspects of complex software systems, hence reducing the time and effort it takes to understand and maintain them. Traces, however, tend to be considerably large which hinders their effective analysis. Existing traces analysis tools rely on some sort of visualization techniques to help software engineers make sense of trace content. Many of these techniques have been studied and found to be limited in many ways. In this paper, we present a novel trace analysis technique that automatically divides the content of a large trace into meaningful segments that correspond to the program's main execution phases such as initializing variables, performing a specific computation, etc. These phases can simplify significantly the exploration of large traces by allowing software engineers to first understand the content of a trace at a high-level before they decide to dig into the details. Our phase detection method is inspired by Gestalt laws that characterize the proximity, similarity, and continuity of the elements of a data space. We model these concepts in the context of execution traces and show how they can be used as gravitational forces that yield the formation of dense groups of trace elements, which indicate candidate phases. We applied our approach to two software systems. The results are very promising.

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Section: What Is An Execution Phase?supporting

confidence: 83%

“…Execution phases can appear at various levels of a program execution [5,8,12]. At the highest level of abstraction, a program execution can be considered as an algorithm or a general procedure for solving a specific problem.…”

Section: What Is An Execution Phase?mentioning

confidence: 99%

A Novel Approach Based on Gestalt Psychology for Abstracting the Content of Large Execution Traces for Program Comprehension

Pirzadeh

Hamou-Lhadj

2011

2011 16th IEEE International Conference on Engineering of Complex Computer Systems

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“…These methods primarily involve removing trivial behaviors, such as repetitive behaviors [6], [10], [14], and implementation details [9]. Another approach is to divide an execution trace into several phases [8], [11], [21]- [23] that correspond with the starting points of tasks.…”

Section: Coping With a Scalability Issue Of A Reverseengineered Sequementioning

confidence: 99%

Identifying Core Objects for Trace Summarization by Analyzing Reference Relations and Dynamic Properties

Noda

Kobayashi

Atsumi

2018

IEICE Trans. Inf. & Syst.

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Kunihiro NODA†a) , Nonmember, Takashi KOBAYASHI †b) , and Noritoshi ATSUMI † †c) , Members SUMMARY Behaviors of an object-oriented system can be visualized as reverse-engineered sequence diagrams from execution traces. This approach is a valuable tool for program comprehension tasks. However, owing to the massiveness of information contained in an execution trace, a reverse-engineered sequence diagram is often afflicted by a scalability issue. To address this issue, many trace summarization techniques have been proposed. Most of the previous techniques focused on reducing the vertical size of the diagram. To cope with the scalability issue, decreasing the horizontal size of the diagram is also very important. Nonetheless, few studies have addressed this point; thus, there is a lot of needs for further development of horizontal summarization techniques. We present in this paper a method for identifying core objects for trace summarization by analyzing reference relations and dynamic properties. Visualizing only interactions related to core objects, we can obtain a horizontally compactified reverseengineered sequence diagram that contains system's key behaviors. To identify core objects, first, we detect and eliminate temporary objects that are trivial for a system by analyzing reference relations and lifetimes of objects. Then, estimating the importance of each non-trivial object based on their dynamic properties, we identify highly important ones (i.e., core objects). We implemented our technique in our tool and evaluated it by using traces from various open-source software systems. The results showed that our technique was much more effective in terms of the horizontal reduction of a reverse-engineered sequence diagram, compared with the state-of-theart trace summarization technique. The horizontal compression ratio of our technique was 134.6 on average, whereas that of the state-of-the-art technique was 11.5. The runtime overhead imposed by our technique was 167.6% on average. This overhead is relatively small compared with recent scalable dynamic analysis techniques, which shows the practicality of our technique. Overall, our technique can achieve a significant reduction of the horizontal size of a reverse-engineered sequence diagram with a small overhead and is expected to be a valuable tool for program comprehension.

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“…Another miss triggered method was suggested by Watanabe et al (2008), with the aim of visualising objects in an object-oriented program. An LRU cache is employed for observing objects that are working for the current phase.…”

Section: Related Workmentioning

confidence: 99%

Efficient memory control for avionics and embedded systems

Weisberg

Wiseman

2013

IJES

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Abstract:Modern aircrafts have seen a significant growth of avionics systems. Most of new aircrafts have systems for navigation, automatic flight control, collision avoidance systems, flight data recorder, weather radar system as well as communication and monitoring systems. All of these systems use embedded computer systems with increasing memory requirements. In order to reduce the electricity consumed by these systems, we suggest detecting the portion of the computer memory that is actually used. The other portion can be temporarily shut down and turned on again when needed. Such a shut down can notably reduce the electricity consumption of the avionics systems.

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Feature-level phase detection for execution trace using object cache

Cited by 24 publications

References 32 publications

A Novel Approach Based on Gestalt Psychology for Abstracting the Content of Large Execution Traces for Program Comprehension

A Novel Approach Based on Gestalt Psychology for Abstracting the Content of Large Execution Traces for Program Comprehension

Identifying Core Objects for Trace Summarization by Analyzing Reference Relations and Dynamic Properties

Efficient memory control for avionics and embedded systems

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