Which log level should developers choose for a new logging statement?

Li, Heng; Shang, Weiyi; Hassan, Ahmed E.

doi:10.1007/s10664-016-9456-2

Cited by 88 publications

(78 citation statements)

References 35 publications

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“…"I think it should probably be an ERROR level, and I missed it in the review (could make an argument either way, I do not feel strongly that it should be ERROR level vs INFO level." Our opinions (i.e., from us and prior studies [72,37]) differ from that of developers' regarding whether such inconsistencies are problematic. On one hand, whether an instance of IL is problematic or not can be subjective.…”

Section: Examplementioning

confidence: 58%

“…Log levels represent the verbosity level of the log and can be used to reduce logging overheads in production (e.g., only logging info level or above) [37,72]. If two or more logging statements have the same static text message, they are identified as duplicate logging statements.…”

Section: Definition and How To Identify Duplicate Logging Statementsmentioning

confidence: 99%

“…Description. Log levels (e.g., fatal, error, info, debug, or trace) allow developers to specify the verbosity of the log message and to reduce logging overhead when needed (e.g., debug is usually disabled in production) [37]. A prior study [72] shows that log level is frequently modified by developers in order to find the most adequate level.…”

Section: Pattern 4: Inconsistent Log Level (Il)mentioning

confidence: 99%

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Characterizing and Detecting Duplicate Logging Code Smells

2019

2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

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School of Graduate StudiesThis is to certify that the thesis prepared By: Zhenhao Li Entitled: Characterizing and Detecting Duplicate Logging Code Smells and submitted in partial fulfillment of the requirements for the degree of Master of Applied Science (Software Engineering) complies with the regulations of this University and meets the accepted standards with respect to originality and quality. Abstract Characterizing and Detecting Duplicate Logging Code Smells Zhenhao Li Developers rely on software logs for a wide variety of tasks, such as debugging, testing, program comprehension, verification, and performance analysis. Despite the importance of logs, prior studies show that there is no industrial standard on how to write logging statements. Recent research on logs often only considers the appropriateness of a log as an individual item (e.g., one single logging statement); while logs are typically analyzed in tandem. In this thesis, we focus on studying duplicate logging statements, which are logging statements that have the same static text message. Such duplications in the text message are potential indications of logging code smells, which may affect developers' understanding of the dynamic view of the system. We manually studied over 3K duplicate logging statements and their surrounding code in four large-scale open source systems:Hadoop, CloudStack, ElasticSearch, and Cassandra. We uncovered five patterns of duplicate logging code smells. For each instance of the code smell, we further manually identify the problematic (i.e., require fixes) and justifiable (i.e., do not require fixes) cases. Then, we contact developers in order to verify our manual study result. We integrated our manual study result and developers' feedback into our automated static analysis tool, DLFinder, which automatically detects problematic duplicate logging code smells. We evaluated DLFinder on the four manually studied systems and four additional systems: Kafka, Flink, Camel and Wicket. In total, combining the results of DLFinder and our manual analysis, we reported 91 problematic code smell instances to developers and all of them have been fixed. This thesis provides an initial step on creating a logging guideline for developers to improve the quality of logging code. DLFinder is also able to detect duplicate logging code smells with high precision and recall.iii

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Section: Examplementioning

confidence: 58%

Section: Definition and How To Identify Duplicate Logging Statementsmentioning

confidence: 99%

Section: Pattern 4: Inconsistent Log Level (Il)mentioning

confidence: 99%

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Characterizing and Detecting Duplicate Logging Code Smells

2019

2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

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“…Event names should be consistent across products and platforms so that it is intuitive to search for them and link them with tangible actions in the product. Depending on the type of the product and the device that it is running on, different options for logging events may be available . Event information should be aggregated and anonymized for its use in computing experiment metrics.…”

Section: The Experimentation Growth Modelmentioning

confidence: 99%

“…Depending on the type of the product and the device that it is running on, different options for logging events may be available. 49 Event information should be aggregated and anonymized for its use in computing experiment metrics. For example, for many metrics, it may be enough to keep a counter of how many times a user used the Direct Flight Experiment features, as opposed to retaining one log for every time the feature was used.…”

Section: Crawl Stagementioning

confidence: 99%

Experimentation growth: Evolving trustworthy A/B testing capabilities in online software companies

Fabijan

Dmitriev²,

McFarland³

et al. 2018

J Software Evolu Process

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Companies need to know how much value their ideas deliver to customers. One of the most powerful ways to accurately measure this is by conducting online controlled experiments (OCEs). To run experiments, however, companies need to develop strong experimentation practices as well as align their organization and culture to experimentation. The main objective of this paper is to demonstrate how to run OCEs at large scale using the experience of companies that succeeded in scaling. Based on case study research at Microsoft, Booking.com, Skyscanner, and Intuit, we present our main contribution—The Experiment Growth Model. This four‐stage model addresses the seven critical aspects of experimentation and can help companies to transform their organizations into learning laboratories where new ideas can be tested with scientific accuracy. Ultimately, this should lead to better products and services.

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An exploratory semantic analysis of logging questions

Gujral

Lal

2021

J Software Evolu Process

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Logging is an integral part of software development. Software practitioners often face issues in software logging, and they post these issues on Q&A websites to take suggestions from the experts. In this study, we perform a three-level empirical analysis of logging questions posted on six popular technical Q&A websites, namely, Stack Overflow (SO), Serverfault (SF), Superuser (SU), Database Administrators (DB), Software Engineering (SE), and Android Enthusiasts (AE). The findings show that logging issues are prevalent across various domains, for example, database, networks, and mobile computing, and software practitioners from different domains face different logging issues. The semantic analysis of logging questions using Latent Dirichlet Allocation (LDA) reveals trends of several existing and new logging topics, such as logging conversion pattern, Android device logging, and database logging. In addition, we observe specific logging topics for each website: DB (log shipping and log file growing/ shrinking), SU (event log and syslog configuration), SF (log analysis and syslog configuration), AE (app install and usage tracking), SE (client server logging and exception logging), and SO (log file creation/deletion, Android emulator logging, and logger class of Log4j).We obtain an increasing trend of logging topics on the SO, SU, and DB websites whereas a decreasing trend of logging topics on the SF website.

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Which log level should developers choose for a new logging statement?

Cited by 88 publications

References 35 publications

Characterizing and Detecting Duplicate Logging Code Smells

Characterizing and Detecting Duplicate Logging Code Smells

Experimentation growth: Evolving trustworthy A/B testing capabilities in online software companies

An exploratory semantic analysis of logging questions

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