Software engineers working in large projects must navigate complex information landscapes. Change Impact Analysis (CIA) is a task that relies on engineers' successful information seeking in databases storing, e.g., source code, requirements, design descriptions, and test case specifications. Several previous approaches to support information seeking are task-specific, thus understanding engineers' seeking behavior in specific tasks is fundamental. We present an industrial case study on how engineers seek information in CIA, with a particular focus on traceability and development artifacts that are not source code. We show that engineers have different information seeking behavior, and that some do not consider traceability particularly useful when conducting CIA. Furthermore, we observe a tendency for engineers to prefer less rigid types of support rather than formal approaches, i.e., engineers value support that allows flexibility in how to practically conduct CIA. Finally, due to diverse information seeking behavior, we argue that future CIA support should embrace individual preferences to identify change impact by empowering several seeking alternatives, including searching, browsing, and tracing.
A number of studies on SE information seeking has targeted issue reports, closely related to our study as the CIA process in the case company is tightly connected with the issue tracker (see ). The number of incoming issue reports in large software engineering projects can be overwhelming, and particularly research on duplicate detection has received much attention. Runeson pioneered issue duplicate detection using standard information retrieval techniques @cite_39 . Several researchers have done similar work, including a replication by Borg that discusses improving findability in the issue tracker using the open source software search library Apache Lucene @cite_31 . Also in issue tracking there is research available on task specific search solutions, e.g., Baysal developed customized issue dashboards based on a grounded theory study of developers' comments @cite_40 .
@cite_40 @cite_31 @cite_39
2014690198 2114651011
Modern software development tools such as issue trackers are often complex and multi-purpose tools that provide access to an immense amount of raw information. Unfortunately, developers sometimes feel frustrated when they cannot easily obtain the particular information they need for a given task; furthermore, the constant influx of new data — the vast majority of which is irrelevant to their task at hand — may result in issues being "dropped on the floor". In this paper, we present a developer-centric approach to issue tracking that aims to reduce information overload and improve developers' situational awareness. Our approach is motivated by a grounded theory study of developer comments, which suggests that customized views of a project's repositories that are tailored to developer-specific tasks can help developers better track their progress and understand the surrounding technical context. From the qualitative study, we uncovered a model of the kinds of information elements that are essential for developers in completing their daily tasks, and from this model we built a tool organized around customized issue-tracking dashboards. Further quantitative and qualitative evaluation demonstrated that this dashboard-like approach to issue tracking can reduce the volume of irrelevant emails by over 99 and also improve support for specific issue-tracking tasks. Context: Duplicate detection is a fundamental part of issue management. Systems able to predict whether a new defect report will be closed as a duplicate, may decrease costs by limiting rework and collecting related pieces of information. Goal: Our work explores using Apache Lucene for large-scale duplicate detection based on textual content. Also, we evaluate the previous claim that results are improved if the title is weighted as more important than the description. Method: We conduct a conceptual replication of a well-cited study conducted at Sony Ericsson, using Lucene for searching in the public Android defect repository. In line with the original study, we explore how varying the weighting of the title and the description affects the accuracy. Results: We show that Lucene obtains the best results when the defect report title is weighted three times higher than the description, a bigger difference than has been previously acknowledged. Conclusions: Our work shows the potential of using Lucene as a scalable solution for duplicate detection.