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
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Feedback from software users constitutes a vital part in the evolution of software projects. By filing issue reports, users help identify and fix bugs, document software code, and enhance the software via feature requests. Many studies have explored issue reports, proposed approaches to enable the submission of higher-quality reports, and presented techniques to sort, categorize and leverage issues for software engineering needs. Who, however, cares about filing issues? What kind of issues are reported in issue trackers? What kind of correlation exist between issue reporting and the success of software projects? In this study, we address the need for answering such questions by performing an empirical study on a hundred thousands of open source projects. After filtering relevant trackers, the study used about 20,000 projects. We investigate and answer various research questions on the popularity and impact of issue trackers. 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. Software and systems traceability is widely accepted as an essential element for supporting many software development tasks. Today's version control systems provide inbuilt features that allow developers to tag each commit with one or more issue ID, thereby providing the building blocks from which project-wide traceability can be established between feature requests, bug fixes, commits, source code, and specific developers. However, our analysis of six open source projects showed that on average only 60 of the commits were linked to specific issues. Without these fundamental links the entire set of project-wide links will be incomplete, and therefore not trustworthy. In this paper we address the fundamental problem of missing links between commits and issues. Our approach leverages a combination of process and text-related features characterizing issues and code changes to train a classifier to identify missing issue tags in commit messages, thereby generating the missing links. We conducted a series of experiments to evaluate our approach against six open source projects and showed that it was able to effectively recommend links for tagging issues at an average of 96 recall and 33 precision. In a related task for augmenting a set of existing trace links, the classifier returned precision at levels greater than 89 in all projects and recall of 50 . 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.