Stratified sampling of execution traces: Execution phases serving as strata

Pirzadeh, Heidar; Shanian, Sara; Hamou-Lhadj, Abdelwahab; Alawneh, Luay; Shafiee, Arya

doi:10.1016/j.scico.2012.11.002

Cited by 21 publications

(15 citation statements)

References 28 publications

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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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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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“…A sample typically represents a subset of the population of manageable size, thus allowing faster data collection and smaller analysis costs. Many previous works, such as [11][12][13][14][15][16], have explored the use of sampling to reduce the size of execution traces and/or the runtime overhead of dynamic analysis tools. Overall, sampling appears to be a valuable tool in dynamic analysis, although sampled traces are not always representative of the original ones, and the results often heavily depend on manual tuning of a variety of parameters.…”

Section: Samplingmentioning

confidence: 99%

Software Streams: Big Data Challenges in Dynamic Program Analysis

Finocchi

2013

Lecture Notes in Computer Science

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Dynamic program analysis encompasses the development of techniques and tools for analyzing computer software by exploiting information gathered from a program at runtime. The impressive amounts of data collected by dynamic analysis tools require efficient indexing and compression schemes, as well as on-line algorithmic techniques for mining relevant information on-the-fly in order to identify frequent events, hidden software patterns, or undesirable behaviors corresponding to bugs, malware, or intrusions. The paper explores how recent results in algorithmic theory for data-intensive scenarios can be applied to the design and implementation of dynamic program analysis tools, focusing on two important techniques: sampling and streaming.

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“…Therefore, many tools and techniques have been developed to tackle this problem. For example, research papers suggest different abstraction techniques (such as [44][45][46][47][48][49]) or different metrics (such as [50]) for large traces to aid program comprehension or verification. In addition, they suggest many trace visualization techniques (e.g., [51][52][53]) that factor human cognition into concrete visualization to make a large trace understandable.…”

Section: Execution Tracesmentioning

confidence: 99%

“…15 shows the number of classes in Weka and OE with various DIT values. Among 1,025 classes in Weka (not accounting for inner classes 48 ) thirty percent (308 classes) have one ancestor (DIT=2). On the other hand, among four classes in OE, there is only one class with one ancestor and the DIT for other classes is equal to one.…”

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Towards Efficient Instrumentation for Reverse-Engineering Object Oriented Software through Static and Dynamic Analyses

Mehrfard¹

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In software engineering, program analysis is usually classified according to static analysis (by analyzing source code) and dynamic analysis (by observing program executions). While static analysis provides inaccurate and imprecise results due to programming language's features (e.g., late binding), dynamic analysis produces more accurate and precise results at runtime at the expense of longer executions to collect traces.One prime mechanism to observe executions in dynamic analysis is to instrument either the code or the binary/byte code. Instrumentation overhead potentially poses a serious threat to the accuracy of the dynamic analysis, especially for time dependent software systems (e.g., real-time software), since it can cause those software systems to go out of synchronization. For instance, in a typical real-time software, the dynamic

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Stratified sampling of execution traces: Execution phases serving as strata

Cited by 21 publications

References 28 publications

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

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

Software Streams: Big Data Challenges in Dynamic Program Analysis

Towards Efficient Instrumentation for Reverse-Engineering Object Oriented Software through Static and Dynamic Analyses

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