Call sequence prediction through probabilistic calling automata

Zhao, Zhijia; Wu, Bo; Zhou, Mingzhou; Ding, Yufei; Sun, Jianhua; Shen, Xipeng; Wu, Youfeng

doi:10.1145/2714064.2660221

Cited by 3 publications

(4 citation statements)

References 41 publications

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“…Collecting data from the field is extremely useful to discover how applications are actually used and support software engineering tasks. For instance, several monitoring techniques collect sequences of function calls to reproduce failures [12], detect malicious behaviors [14], debug applications [15], profile software [9], optimize applications [16], and mine models [17,18]. If retrieving this data is indeed useful, knowing the impact of the monitoring activity on the user experience is also extremely important.…”

Section: Discussionmentioning

confidence: 99%

“…Studying the behavior of an application in terms of the function calls produced under different circumstances is in fact both common and useful. For example, sequences of function calls extracted from the field can be used to reproduce failures [12], detect malicious behaviors [14], debug applications [15], profile software [9], optimize applications [16], and mine models [17,18,19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

In-The-Field Monitoring of Functional Calls: Is It Feasible?

Cornejo,

Briola,

Micucci

et al. 2020

Preprint

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Collecting data about the sequences of function calls executed by an application while running in the field can be useful to a number of applications, including failure reproduction, profiling, and debugging. Unfortunately, collecting data from the field may introduce annoying slowdowns that negatively affect the quality of the user experience.So far, the impact of monitoring has been mainly studied in terms of the overhead that it may introduce in the monitored applications, rather than considering if the introduced overhead can be really recognized by users. In this paper we take a different perspective studying to what extent collecting data about sequences of function calls may impact the quality of the user experience, producing recognizable effects. Interestingly we found that, depending on the nature of the executed operation and its execution context, users may tolerate a non-trivial overhead. This information can be potentially exploited to collect significant amount of data without annoying users.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In-The-Field Monitoring of Functional Calls: Is It Feasible?

Cornejo,

Briola,

Micucci

et al. 2020

Preprint

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“…Although the concept behind Delayed Saving is valid for different kinds of events and data that can be monitored, let us specifically consider the case of a monitor that must collect the sequence of method calls that are executed by an application, to later reconstruct and analyze its internal behavior. Tracing method calls is common to many techniques and applications (e.g., reproducing failures [16], detecting malicious behaviors [21], debugging applications [22], profiling software [2], optimizing applications [23], and mining models [18], [24], [25] which is also the case we studied empirically in our evaluation). In such a case, Delayed Saving collects the sequence of methods that are executed and stores the references to the objects used as parameter during the working state.…”

Section: Delayed Savingmentioning

confidence: 99%

Field Monitoring With Delayed Saving

et al. 2019

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Field monitoring techniques can collect data about the behavior of software applications while running in the field, with real users and real data. Developers can exploit the information extracted from the field to timely improve, tune, and fix their systems, anticipating feedback by users. It is, however, challenging to extract a relevant amount of information from field executions without introducing significant overhead. This paper addresses this challenge by studying how to inexpensively trace data in-memory while postponing save operations to idle time, so that the operations requested by users are exposed to a negligible overhead only. In particular, this paper presents delayed saving, a technique that efficiently traces references to objects, opportunistically saving information only when the monitored application is not serving any user request. Storing references and postponing save operations may introduce inaccuracy in the collected data, that is, a lately saved object might be in a different state compared to the state of the object at the time it was traced. Our evaluation shows that the level of inaccuracy introduced by delayed saving is limited compared to its efficiency and low intrusiveness. INDEX TERMS Monitoring, tracing, dynamic analysis.

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“…While some solutions, such as simple crash reporting features, require collecting relatively few data for a short amount of time (e.g., a snapshot of the system at the time of the crash [10], [11], [12]), many interesting and sophisticated approaches require monitoring applications more extensively. For example, many approaches collect sequences of method calls to reproduce failures [13], detect malicious behaviors [14], debug applications [15], profile software [2], optimize applications [16], and mine models [5], [17], [18]. Unfortunately, extensively recording sequences of function calls might introduce an annoying overhead and cause unacceptable slowdowns, as for example experienced by Jin et al [13].…”

Section: Introductionmentioning

confidence: 99%

CBR: Controlled Burst Recording

Cornejo¹,

Briola²,

Micucci³

et al. 2020

Preprint

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Collecting traces from software running in the field is both useful and challenging. Traces may indeed help revealing unexpected usage scenarios, detecting and reproducing failures, and building behavioral models that reflect how the software is actually used. On the other hand, recording traces is an intrusive activity that may annoy users, negatively affecting the usability of the applications, if not properly designed.In this paper we address field monitoring by introducing Controlled Burst Recording, a monitoring solution that can collect comprehensive runtime data without compromising the quality of the user experience. The technique encodes the knowledge extracted from the monitored application as a finite state model that both represents the sequences of operations that can be executed by the users and the corresponding internal computations that might be activated by each operation.Our initial assessment with information extracted from Ar-goUML shows that Controlled Burst Recording can reconstruct behavioral information more effectively than competing sampling techniques, with a low impact on the system response time.

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Call sequence prediction through probabilistic calling automata

Cited by 3 publications

References 41 publications

In-The-Field Monitoring of Functional Calls: Is It Feasible?

In-The-Field Monitoring of Functional Calls: Is It Feasible?

Field Monitoring With Delayed Saving

CBR: Controlled Burst Recording

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