Experience Report: Log-Based Behavioral Differencing

Goldstein, Maayan; Raz, Danny; Segall, Itai

doi:10.1109/issre.2017.14

Cited by 24 publications

(14 citation statements)

References 23 publications

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“…We differ in addressing by similar techniques the traces of general-purpose programs. Analysis of execution traces is a very active research area in software engineering (e.g., recent works are [AMP18,GRS17]) and process engineering (e.g., [BvdA12]). We believe that a model-driven management of traces can enhance the interoperability of such solutions.…”

Section: Class Methods Statement Test_ma_0mentioning

confidence: 99%

Source-Code Level Regression Test Selection: the Model-Driven Way.

Fosse¹,

Mottu²,

Tisi³

et al. 2019

JOT

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In order to ensure that existing functionalities have not been impacted by recent program changes, test cases are regularly executed during regression testing (RT) phases. The RT time becomes problematic as the number of test cases is growing. Regression test selection (RTS) aims at running only the test cases that have been impacted by recent changes. RTS reduces the duration of regression testing and hence its cost. In this paper, we present a model-driven approach for RTS. Execution traces are gathered at runtime, and injected in a static source-code model. We use this resulting model to identify and select all the test cases that have been impacted by changes between two revisions of the program. Our MDE approach allows modularity in the granularity of changes considered. In addition, it offers better reusability than existing RTS techniques: the trace model is persistent and standardised. Furthermore, it enables more interoperability with other model-driven tools, enabling further analysis at different levels of abstraction (e.g., energy consumption). We evaluate our approach by applying it to four well-known opensource projects. Results show that our MDE proposal can effectively reduce the execution time of RT, by up to 32 % in our case studies. The overhead induced by the model building makes our approach slower than dedicated RTS tools, but the reuse of trace models for further analysis compensates for the time.

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Section: Class Methods Statement Test_ma_0mentioning

confidence: 99%

Source-Code Level Regression Test Selection: the Model-Driven Way.

Fosse¹,

Mottu²,

Tisi³

et al. 2019

JOT

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“…Execution logs have been extensively studied in such contexts as anomaly detection [4] [20], identification of software components [24], component behavior discovery [17], process mining [28], behavioral differencing [12], failure diagnosis [25], fault localization [30], invariant inference [5], and performance diagnosis [10] [26]. In this section, we focus on automatic analysis of execution logs.…”

Section: Related Workmentioning

confidence: 99%

“…Goldstein et al [12] analyze system logs, automatically infer Finite State Automata, and compare the inferred behavior to the expected behavior. However, they work on system logs with predefined states while we identify these states (templates) first.…”

Section: Related Workmentioning

confidence: 99%

Runtime Monitoring in Continuous Deployment by Differencing Execution Behavior Model

Gupta¹,

Mandal²,

Dasgupta³

et al. 2018

Service-Oriented Computing

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DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

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“…Furthermore, the importance of logging is also recognized by developers: a recent survey reported that 96% of a group of experienced developers from a leading software company "strongly agree/agree that logging statements are important in system development and maintenance" [13]. Indeed, the information contained in log files can be used for a variety of purposes, such as process mining [15,30], anomaly detection [4,12,14], behavioral differencing [14], fault localization [33], invariant inference [5], performance diagnosis [21], and offline trace checking [3].…”

Section: Introductionmentioning

confidence: 99%

A search-based approach for accurate identification of log message formats

Messaoudi

Panichella

Bianculli

et al. 2018

Proceedings of the 26th Conference on Program Comprehension

120

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Many software engineering activities process the events contained in log files. However, before performing any processing activity, it is necessary to parse the entries in a log file, to retrieve the actual events recorded in the log. Each event is denoted by a log message, which is composed of a fixed part-called (event) template-that is the same for all occurrences of the same event type, and a variable part, which may vary with each event occurrence. The formats of log messages, in complex and evolving systems, have numerous variations, are typically not entirely known, and change on a frequent basis; therefore, they need to be identified automatically.The log message format identification problem deals with the identification of the different templates used in the messages of a log. Any solution to this problem has to generate templates that meet two main goals: generating templates that are not too general, so as to distinguish different events, but also not too specific, so as not to consider different occurrences of the same event as following different templates; however, these goals are conflicting.In this paper, we present the MoLFI approach, which recasts the log message identification problem as a multi-objective problem. MoLFI uses an evolutionary approach to solve this problem, by tailoring the NSGA-II algorithm to search the space of solutions for a Pareto optimal set of message templates. We have implemented MoLFI in a tool, which we have evaluated on six real-world datasets, containing log files with a number of entries ranging from 2K to 300K. The experiments results show that MoLFI extracts by far the highest number of correct log message templates, significantly outperforming two state-of-the-art approaches on all datasets. CCS CONCEPTS• Software and its engineering → Search-based software engineering;

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Experience Report: Log-Based Behavioral Differencing

Cited by 24 publications

References 23 publications

Source-Code Level Regression Test Selection: the Model-Driven Way.

Source-Code Level Regression Test Selection: the Model-Driven Way.

Runtime Monitoring in Continuous Deployment by Differencing Execution Behavior Model

A search-based approach for accurate identification of log message formats

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