Automated Reuse Recommendation of Product Line Assets Based on Natural Language Requirements

Abbas, Muhammad; Saadatmand, Mehrdad; Enoiu, Eduard Paul; Sundamark, Daniel; Lindskog, Claes

doi:10.1007/978-3-030-64694-3_11

Cited by 8 publications

(1 citation statement)

References 22 publications

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“…A focus group study was planned to collect qualitative insights about the results, following the guidelines by Breen et al [9] and supplemented it with our experience in conducting and reporting focus groups in industry [1][2][3] (see the bottom half of Figure 2). An instrument was drafted and subsequently validated and refined in a pilot session with one expert from the company.…”

Section: Discussionmentioning

confidence: 99%

Making Sense of Failure Logs in an Industrial DevOps Environment

Abbas¹,

Ali²,

Moghadam³

et al. 2023

Preprint

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Processing and reviewing nightly test execution failure logs for large industrial systems is a tedious activity. Furthermore, multiple failures might share one root/common cause during test execution sessions, and the review might therefore require redundant efforts. This paper presents the Log-Grouper approach for automated grouping of failure logs to aid root/common cause analysis and for enabling the processing of each log group as a batch. LogGrouper uses state-of-art natural language processing and clustering approaches to achieve meaningful log grouping. The approach is evaluated in an industrial setting in both a qualitative and quantitative manner. Results show that LogGrouper produces good quality groupings in terms of our two evaluation metrics (Silhouette Coefficient and Calinski-Harabasz Index) for clustering quality. The qualitative evaluation shows that experts perceive the groups as useful, and the groups are seen as an initial pointer for root cause analysis and failure assignment.

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Section: Discussionmentioning

confidence: 99%