An Approach to Recommendation of Verbosity Log Levels Based on Logging Intention

Anu, Han; Chen, Jie; Shi, Wenchang; Hou, Jianwei; Liang, Bin; Qin, Bo

doi:10.1109/icsme.2019.00022

Cited by 18 publications

(11 citation statements)

References 19 publications

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“…Both studies highlight the relationship of the log message and associate severity of a log statement. In another study, Anu et al (2019) also proposes a classifier for log level recommendation. They focus on log statements located on if-else blocks and exception handling.…”

Section: Resultsmentioning

confidence: 99%

“…,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%

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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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Section: Resultsmentioning

confidence: 99%

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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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“…Our approach is reasonably extendable to predict LVL and VAR alongside the LSD suggestion. Since we have access to the source code of the method that we are predicting the logging statement for and its clone pair, a reasonable starting point is to suggest the same LVL as of its clone pair, and then augment it with additional learning approaches such as [12,45,48] for more sophisticated LVL prediction. For example, our analysis for the evaluated projects in Figure 5 shows that code clones match in their verbosity levels in the range of (92, 97)%.…”

Section: Discussionmentioning

confidence: 99%

Borrowing from Similar Code: A Deep Learning NLP-Based Approach for Log Statement Automation

Gholamian¹,

Ward²

2021

Preprint

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Software developers embed logging statements inside the source code as an imperative duty in modern software development as log files are necessary for tracking down runtime system issues and troubleshooting system management tasks. Prior research has emphasized the importance of logging statements in the operation and debugging of software systems. However, the current logging process is mostly manual, and thus, proper placement and content of logging statements remain as challenges. To overcome these challenges, methods that aim to automate log placement and predict its content, i.e., 'where and what to log', are of high interest. Thus, we focus on predicting the location (i.e., where) and description (i.e., what) for log statements by utilizing source code clones and natural language processing (NLP), as these approaches provide additional context and advantage for log prediction. Specifically, we guide our research with three research questions (RQs): (RQ1) how similar code snippets, i.e., code clones, can be leveraged for log statements prediction? (RQ2) how the approach can be extended to automate log statements' descriptions? and (RQ3) how effective the proposed methods are for log location and description prediction? To pursue our RQs, we perform an experimental study on seven open-source Java projects. We introduce an updated and improved log-aware code-clone detection method to predict the location of logging statements (RQ1). Then, we incorporate natural language processing (NLP) and deep learning methods to automate the log statements' description prediction (RQ2). Our analysis shows that our hybrid NLP and code-clone detection approach (NLP CC'd) outperforms conventional clone detectors in finding log statement locations on average by 15.60% and achieves 40.86% higher performance on BLEU and ROUGE scores for predicting the description of logging statements when compared to prior research (RQ3). Our work demonstrates the effectiveness of borrowing context from similar code snippets for automated log location and description prediction.CCS Concepts: • Software and its engineering → Software libraries and repositories; Software configuration management and version control systems; Software maintenance tools; Maintaining software; System administration.

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“…J stands for journal, and C stands for conference, symposium, or workshop. The list of additional venues with only one publication include: arXiv [123], [124], CSUR [65], TOCS [67], TPDS [125], IEEE Software [57], TSMCA [126], TNSM [127], SP&E [128], JCST [129], HiPC [130], CSRD [131], IJCAI [132], NSDI [7], CIKM [133], CCS [5], SIGKDD [134], IMC [135], MASCOTS [136], WSE [137], IWQoS [138], ICWS [139], ICPC [140], ICSEM [141], ICPE [142], ICC [143], Middleware [144], VLDB [15], CNSM [13], APSYS [145], HICSS [146], and COMPSAC [147].…”

Section: Logging Challengesmentioning

confidence: 99%

“…Anu et al [141] -Proposes a method to make the logging level decisions by understanding the logging intentions.…”

Section: Asmentioning

confidence: 99%

A Comprehensive Survey of Logging in Software: From Logging Statements Automation to Log Mining and Analysis

Gholamian¹,

Ward²

2021

Preprint

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Logs are widely used to record runtime information of software systems, such as the timestamp and the importance of an event, the unique ID of the source of the log, and a part of the state of a task's execution. The rich information of logs enables system developers (and operators) to monitor the runtime behaviors of their systems and further track down system problems and perform analysis on log data in production settings. However, the prior research on utilizing logs is scattered and that limits the ability of new researchers in this field to quickly get to the speed and hampers currently active researchers to advance this field further. Therefore, this paper surveys and provides a systematic literature review and mapping of the contemporary logging practices and log statements' mining and monitoring techniques and their applications such as in system failure detection and diagnosis. We study a large number of conference and journal papers that appeared on top-level peer-reviewed venues. Additionally, we draw high-level trends of ongoing research and categorize publications into subdivisions. In the end, and based on our holistic observations during this survey, we provide a set of challenges and opportunities that will lead the researchers in academia and industry in moving the field forward.

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An Approach to Recommendation of Verbosity Log Levels Based on Logging Intention

Cited by 18 publications

References 19 publications

Log-based software monitoring: a systematic mapping study

Log-based software monitoring: a systematic mapping study

Borrowing from Similar Code: A Deep Learning NLP-Based Approach for Log Statement Automation

A Comprehensive Survey of Logging in Software: From Logging Statements Automation to Log Mining and Analysis

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