Guiding log revisions by learning from software evolution history

Li, Shanshan; Niu, Xu; Jia, Zhouyang; Liao, Xiangke; Wang, Ji; Li, Tao

doi:10.1007/s10664-019-09757-y

Cited by 13 publications

(16 citation statements)

References 26 publications

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“…Nguyen et al used Jsync to track cloned code in the software evolution process [28]. Moreover, many studies used GumTree to analyze code patterns of changes such as bug-fixing changes [5], [13], [18], [21], [23], [29], logging changes [22] and changes to online code examples [34]. Also, prior work trained models based on the edit actions of changes that are calculated using GumTree [7], [15], [24], [31], [32].…”

Section: B Use Of Ast Mapping Algorithmsmentioning

confidence: 99%

“…Also, prior work trained models based on the edit actions of changes that are calculated using GumTree [7], [15], [24], [31], [32]. Such models are used to recommend changes such as patches [32] and logging changes [22]. Different from them, we focus on evaluating AST mapping algorithms instead of using the algorithms to analyze changes.…”

Section: B Use Of Ast Mapping Algorithmsmentioning

confidence: 99%

“…Accurate mappings lead to accurate edit actions that can reflect a developer's intent. Many prior studies apply AST mapping algorithms to calculate edit actions for further analyses, e.g., API recommendation [27], mining code change patterns [22], and § Corresponding author. automated program repair [32].…”

Section: Introductionmentioning

confidence: 99%

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A Differential Testing Approach for Evaluating Abstract Syntax Tree Mapping Algorithms

Fan

Xia

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

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syntax tree (AST) mapping algorithms are widely used to analyze changes in source code. Despite the foundational role of AST mapping algorithms, little effort has been made to evaluate the accuracy of AST mapping algorithms, i.e., the extent to which an algorithm captures the evolution of code. We observe that a program element often has only one best-mapped program element. Based on this observation, we propose a hierarchical approach to automatically compare the similarity of mapped statements and tokens by different algorithms. By performing the comparison, we determine if each of the compared algorithms generates inaccurate mappings for a statement or its tokens. We invite 12 external experts to determine if three commonly used AST mapping algorithms generate accurate mappings for a statement and its tokens for 200 statements. Based on the experts' feedback, we observe that our approach achieves a precision of 0.98-1.00 and a recall of 0.65-0.75. Furthermore, we conduct a large-scale study with a dataset of ten Java projects containing a total of 263,165 file revisions. Our approach determines that GumTree, MTDiff and IJM generate inaccurate mappings for 20%-29%, 25%-36% and 21%-30% of the file revisions, respectively. Our experimental results show that state-of-the-art AST mapping algorithms still need improvements.

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Section: B Use Of Ast Mapping Algorithmsmentioning

confidence: 99%

Section: B Use Of Ast Mapping Algorithmsmentioning

confidence: 99%

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A Differential Testing Approach for Evaluating Abstract Syntax Tree Mapping Algorithms

Fan

Xia

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

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“…The approach leverages execution logs without instrumenting the code because instrumentation overhead is not possible in a fast evolving production software [31]. Nevertheless, execution paths are successfully captured from the existing logs because in practice, sufficient logging is done to facilitate runtime monitoring [6] [16].…”

Section: Proposed Approachmentioning

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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“…,Chen & Jiang (2017b),Shang et al (2014),Shang, Nagappan & Hassan (2015),Pecchia et al (2015),Kabinna et al (2016),,Zeng et al (2019) to log, where to log, and how to logChen & Jiang (2017a),Hassani et al (2018),Fu et al (2014a),Zhu et al (2015),Li et al (2018),Li, Shang & Hassan (2017),He et al (2018a),Li et al (2019a),Liu et al (2019b),Anu et al (2019),Zhi et al (2019) 11Log Infrastructure: Techniques to enable and fulfil the requirements of the analysis process 16Parsing Extraction of log templates from raw log dataAharon et al (2009), Makanju, Zincir-Heywood & Milios (2009), Makanju, Zincir-Heywood & Milios (2012), Liang et al (2007), Gainaru et al (2011), Hamooni et al (2016), Zhou et al (2010), Lin et al (2016), Tang & Li (2010), He et al (2016a), He et al (2018b), Zhu et al (2019), Agrawal, Karlupia & Gupta (2019) 13 Storage Efficient persistence of large datasets of logs Lin et al (2015), Mavridis & Karatza (2017), Liu et al (2019a) 3 Log Analysis: Insights from processed log data 68 Anomaly detection Detection of abnormal behaviour Tang & Iyer (1992), Oliner & Stearley (2007), Lim, Singh & Yajnik (2008), Xu et al (2009b), Xu et al (2009a), Fu et al (2009), Ghanbari, Hashemi & Amza (2014), Gao et al (2014), Juvonen, Sipola & Hämäläinen (2015), Farshchi et al (2015), He et al (2016b), Nandi et al (2016), Du et al (2017), Bertero et al (2017), Lu et al (2017), Debnath et al (2018), Bao et al (2018), Farshchi et al (2018), Zhang et al (2019), Meng et al (2019) 20 Security and privacy Intrusion and attack detection Oprea et al (2015), Chu et al (2012), Yoon & Squicciarini (2014), Yen et al (2013), Barse & Jonsson (2004), Abad et al (2003), Prewett (2005), Butin & Le Métayer (2014), Goncalves, Bota & Correia (2015) 9 Root cause analysis Accurate failure identification and impact analysis Gurumdimma et al (2016), Kimura et al (2014), Pi et al (2018), Chuah et al (2013), Zheng et al (2011), Ren et al (2019) 6 Failure prediction Anticipating failures that leads a system to an unrecoverable state Wang et al (2017), Fu et al (2014b), Russo, Succi & Pedrycz (2015), Khatuya et al (2018), Shalan & Zulkernine (2013), Fu et al (2012) 6 Quality assurance Logs as support for quality assurance activities Andrews (1998), Andrews & Zhang (2000), Andrews & Zhang (2003), Chen et al checking Ulrich et al (2003), Mariani & Pastore (2008), Tan et al (2010), Beschastnikh et al (2014), Wu, Anchuri & Li (2017), Awad & Menasce (2016), Kc & Gu (2011), Lou et al (2010), Steinle et al (2006), Di Martino, Cinque & of systems (e.g., reliability, performance) Banerjee, Srikanth & Cukic (2010), Tian, Rudraraju & Li (2004), Huynh & Miller (2009), El-Sayed & Schroeder (2013), Ramakrishna et al (2017), Park et al (2017)6Log pl...…”

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confidence: 99%

Log-based software monitoring: a systematic mapping study

Cândido

Aniche

Deursen

2021

PeerJ Computer Science

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Modern software development and operations rely on monitoring to understand how systems behave in production. The data provided by application logs and runtime environment are essential to detect and diagnose undesired behavior and improve system reliability. However, despite the rich ecosystem around industry-ready log solutions, monitoring complex systems and getting insights from log data remains a challenge. Researchers and practitioners have been actively working to address several challenges related to logs, e.g., how to effectively provide better tooling support for logging decisions to developers, how to effectively process and store log data, and how to extract insights from log data. A holistic view of the research effort on logging practices and automated log analysis is key to provide directions and disseminate the state-of-the-art for technology transfer. In this paper, we study 108 papers (72 research track papers, 24 journals, and 12 industry track papers) from different communities (e.g., machine learning, software engineering, and systems) and structure the research field in light of the life-cycle of log data. Our analysis shows that (1) logging is challenging not only in open-source projects but also in industry, (2) machine learning is a promising approach to enable a contextual analysis of source code for log recommendation but further investigation is required to assess the usability of those tools in practice, (3) few studies approached efficient persistence of log data, and (4) there are open opportunities to analyze application logs and to evaluate state-of-the-art log analysis techniques in a DevOps context.

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Guiding log revisions by learning from software evolution history

Cited by 13 publications

References 26 publications

A Differential Testing Approach for Evaluating Abstract Syntax Tree Mapping Algorithms

A Differential Testing Approach for Evaluating Abstract Syntax Tree Mapping Algorithms

Runtime Monitoring in Continuous Deployment by Differencing Execution Behavior Model

Log-based software monitoring: a systematic mapping study

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