Exploiting text mining techniques in the analysis of execution traces

Pirzadeh, Heidar; Hamou-Lhadj, Abdelwahab; Shah, Mohak

doi:10.1109/icsm.2011.6080789

Cited by 13 publications

(13 citation statements)

References 26 publications

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“…Random sampling may generate in samples that are not representative of the original trace. This work was extended [16] by extracting higher-level views that characterize the relevant information about execution traces.…”

Section: Related Workmentioning

confidence: 99%

SCAN: An Approach to Label and Relate Execution Trace Segments

Medini

Antoniol

Guéhéneuc

et al. 2012

2012 19th Working Conference on Reverse Engineering

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Identifying concepts in execution traces is a task often necessary to support program comprehension or maintenance activities. Several approaches-static, dynamic or hybrid-have been proposed to identify cohesive, meaningful sequence of methods in execution traces. However, none of the proposed approaches is able to label such segments and to identify relations between segments of the same trace.This paper present SCAN (Segment Concept AssigNer) an approach to assign labels to sequences of methods in execution traces, and to identify relations between such segments. SCAN uses information retrieval methods and formal concept analysis to produce sets of words helping the developer to understand the concept implemented by a segment. Specifically, formal concept analysis allows SCAN to discover commonalities between segments in different trace areas, as well as terms more specific to a given segment and high level relations between segments.The paper describes SCAN along with a preliminary manual validation-upon execution traces collected from usage scenarios of JHotDraw and ArgoUML-of SCAN accuracy in assigning labels representative of concepts implemented by trace segments.

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Section: Related Workmentioning

confidence: 99%

SCAN: An Approach to Label and Relate Execution Trace Segments

Medini

Antoniol

Guéhéneuc

et al. 2012

2012 19th Working Conference on Reverse Engineering

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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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“…They demonstrate two different styles of concept visualization, which show the proportion of overall time spent in each concept and the sequence of concept execution, respectively. Pirzadeh et al [2011] propose a trace exploration approach based on examining trace execution phases by adopting the Term Frequency, Inverse Document Frequency (TF-IDF) technique and the cosine similarity measure. Benomar et al [2014] propose an automatic approach for the detection of high-level execution phases from previously recorded execution traces, based on object lives, and without the specification of parameters or thresholds.…”

Section: Quality Of the Real-time Detectormentioning

confidence: 99%

“…Request permissions from permissions@acm.org. understanding Peleg and Mendelson [2007], Singer and Kirkham [2008], Pirzadeh et al [2011], Benomar et al [2014]. They are further interested in phases as a program runs in order to determine whether the program is operating as expected.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Program-Specific Phase Change Detection for Java Programs

Chiu

Marlin

Moss

2016

Proceedings of the 13th International Conference on Principles and Practices of Programming on the Java Platform: Virtual Machi

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It is well-known that programs tend to have multiple phases in their execution. Because phases have impact on micro-architectural features such as caches and branch predictors, they are relevant to program performance Xian et al. [2007], Roh et al. [2009], Gu and Verbrugge [2008] and energy consumption. They are also relevant to detecting whether a program is executing as expected or is encountering unusual or exceptional conditions, a software engineering and program monitoring concern Peleg and Mendelson [2007], Singer and Kirkham [2008], Pirzadeh et al. [2011], Benomar et al. [2014]. We offer here a method for real-time phase change detection in Java programs. After applying a training protocol to a program of interest, our method can detect phase changes at run time for that program with good precision and recall (compared with a "ground truth" definition of phases) and with small performance impact (average less than 2%). We also offer improved methodology for evaluating phase change detection mechanisms. In sum, our approach offers the first known implementation of real-time phase detection for Java programs. CCS Concepts •Computer systems organization → Real-time systems; •Computing methodologies → Machine learning; •Software and its engineering → Runtime environments;

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Exploiting text mining techniques in the analysis of execution traces

Cited by 13 publications

References 26 publications

SCAN: An Approach to Label and Relate Execution Trace Segments

SCAN: An Approach to Label and Relate Execution Trace Segments

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

Real-Time Program-Specific Phase Change Detection for Java Programs

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