Log mining to re-construct system behavior: An exploratory study on a large telescope system

Pettinato, Michèle; Gil, Juan Pablo; Galeas, Patricio; Russo, Barbara

doi:10.1016/j.infsof.2019.06.011

Cited by 15 publications

(12 citation statements)

References 17 publications

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“…Pooling refers to aggregating similar (e.g., semantically or temporally) documents into a single document (Mehrotra et al 2013). For example, among the papers analysed, Pettinato et al (2019) used temporal pooling and combined short log messages into a single document based on a temporal order. Contextualization refers to creating subsets of documents according to a type of context; considering tweets as documents, the type of context can refer to time, user and hashtags associated with tweets (Tang et al 2013).…”

Section: Documents and Parameters For Topic Modelsmentioning

confidence: 99%

“…-Twelve papers (Bagherzadeh and Khatchadourian 2019;Ahmed and Bagherzadeh 2018;Martin et al 2015;Hindle et al 2013;Pagano and Maalej 2013;Zou et al 2017;Pettinato et al 2019;Layman et al 2016;Ray et al 2014;Tiarks and Maalej 2014;Mezouar et al 2018;Abdellatif et al 2020) acknowledged that how topics were named could be a threat to validity. For example, Layman et al (2016) mentioned that they did not evaluate the accuracy of the manual topic naming, which was based on their expertise.…”

Section: Rq4: Assigning Names To Topicsmentioning

confidence: 99%

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Topic modeling in software engineering research

2021

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Topic modeling using models such as Latent Dirichlet Allocation (LDA) is a text mining technique to extract human-readable semantic “topics” (i.e., word clusters) from a corpus of textual documents. In software engineering, topic modeling has been used to analyze textual data in empirical studies (e.g., to find out what developers talk about online), but also to build new techniques to support software engineering tasks (e.g., to support source code comprehension). Topic modeling needs to be applied carefully (e.g., depending on the type of textual data analyzed and modeling parameters). Our study aims at describing how topic modeling has been applied in software engineering research with a focus on four aspects: (1) which topic models and modeling techniques have been applied, (2) which textual inputs have been used for topic modeling, (3) how textual data was “prepared” (i.e., pre-processed) for topic modeling, and (4) how generated topics (i.e., word clusters) were named to give them a human-understandable meaning. We analyzed topic modeling as applied in 111 papers from ten highly-ranked software engineering venues (five journals and five conferences) published between 2009 and 2020. We found that (1) LDA and LDA-based techniques are the most frequent topic modeling techniques, (2) developer communication and bug reports have been modelled most, (3) data pre-processing and modeling parameters vary quite a bit and are often vaguely reported, and (4) manual topic naming (such as deducting names based on frequent words in a topic) is common.

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Section: Documents and Parameters For Topic Modelsmentioning

confidence: 99%

Section: Rq4: Assigning Names To Topicsmentioning

confidence: 99%

Topic modeling in software engineering research

2021

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“…Therefore, understanding BDS behaviors and tracing back the observed errors to their origins rely on distributed log files that is inherently difficult. This may require mining of the log files [Pettinato et al 2019] and eventually needs to develop methods for sophisticated distributed debugging. -Verification methods.…”

Section: Bds Quality Assurancementioning

confidence: 99%

“…32 In addition, there is no suitable testing approach focusing on Velocity as a Big Data characteristic. Instead, the current effort of research community is mostly on Volume and Variety of data [Pettinato et al 2019]. However, Auer and Felderer [2018] reveal a fundamental weakness of existing ML testing solutions as the data dependent behavior of an ML algorithm results in a limited reasoning about its later quality.…”

Section: Software Testingmentioning

confidence: 99%

Big Data Systems

Davoudian

Liu

2020

ACM Comput. Surv.

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Big Data Systems (BDSs) are an emerging class of scalable software technologies whereby massive amounts of heterogeneous data are gathered from multiple sources, managed, analyzed (in batch, stream or hybrid fashion), and served to end-users and external applications. Such systems pose specific challenges in all phases of software development lifecycle and might become very complex by evolving data, technologies, and target value over time. Consequently, many organizations and enterprises have found it difficult to adopt BDSs. In this article, we provide insight into three major activities of software engineering in the context of BDSs as well as the choices made to tackle them regarding state-of-the-art research and industry efforts. These activities include the engineering of requirements, designing and constructing software to meet the specified requirements, and software/data quality assurance. We also disclose some open challenges of developing effective BDSs, which need attention from both researchers and practitioners.

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“…Increasingly in the last decade, organizations rely on the availability of log-data to improve and support their business processes in competitive and rapidly changing environments [26]. Robust process mining techniques have been developed in recent years in several domains ranging from web page contents and usage [27][28][29], education and e-learning platforms [30,31] and software engineering to detect anomalies and errors in systems processes [32]. Despite the growing interest in log-based process analysis, very few, although very promising, studies-have been undertaken to exploit the potentialities of this new data source in the design field [20,22,33].…”

Section: Introductionmentioning

confidence: 99%

Geodesign Process Analytics: Focus on Design as a Process and Its Outcomes

et al. 2019

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This paper argues that the opportunities offered by currently available collaborative Planning Support Systems (PSS) are useful not only for applying a systems approach and coordinating actors in the planning process, but also for tracking the evolution of design alternatives toward a final plan. The availability of process log-data in the latest PSS opens new paths for understanding (geo)design dynamics. With the aim of taking full advantage of this new data source, a novel Geodesign Process Analytics is described in detail from log-data extraction and pre-processing methods and tools to the development of the set of spatial, performance, temporal and design evolution indicators. The study also demonstrates how the proposed measures are appropriate for display in a dynamic dashboard, making available a real-time process analysis tool to the team coordinators, thus supporting their leading role in facilitating the geodesign process. The research assumptions were tested using the Geodesignhub PSS and data from a geodesign study developed within the International Geodesign Collaboration.

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Log mining to re-construct system behavior: An exploratory study on a large telescope system

Cited by 15 publications

References 17 publications

Topic modeling in software engineering research

Topic modeling in software engineering research

Big Data Systems

Geodesign Process Analytics: Focus on Design as a Process and Its Outcomes

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