A discriminative model approach for accurate duplicate bug report retrieval

Sun, Chengnian; Lo, David; Wang, Xiaoyin; Jiang, Jing; Khoo, Siau‐Cheng

doi:10.1145/1806799.1806811

Cited by 279 publications

(186 citation statements)

References 19 publications

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“…Robillard et al presented diverse applications of recommendation systems in software engineering [56]. Some of the applications include recommendation system for: requirements engineering tasks such as finding experts for development tasks [46] and supporting build process [60], source code based tasks such as the correct usage of APIs [79] and code refactoring [9], and bug related tasks such as duplicate bug detection [65][67] and bug triaging [26]. However, the application of recommendation systems for reducing user input requests (both real and tactical) in IT support ticket resolution process is not yet explored which is the focus of the presented work.…”

Section: Application Of Recommendation Systems In Software Engineeringmentioning

confidence: 99%

Reducing user input requests to improve IT support ticket resolution process

et al. 2017

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Management and maintenance of IT infrastructure resources such as hardware, software and network is an integral part of software development and maintenance projects. Service management ensures that the tickets submitted by users, i.e. software developers, are serviced within the agreed resolution times. Failure to meet those times induces penalty on the service provider. To prevent a spurious penalty on the service provider, non-working hours such as waiting for user inputs are not included in the measured resolution time, that is, a service level clock pauses its timing. Nevertheless, the user interactions slow down the resolution process, that is, add to user experienced resolution time and degrade user experience. Therefore, this work is motivated by the need to analyze and reduce user input requests in tickets' life cycle.To address this problem, we analyze user input requests and investigate their impact on user experienced resolution time. We distinguish between input requests of two types: real, seeking information from the user to process the ticket and tactical, when no information is asked but the user input request is raised merely to pause the service level clock. Next, we propose a system that preempts a user at the time of ticket submission to provide additional information that the analyst, a person responsible for servicing the ticket, is likely to ask, thus reducing real user input requests. Further, we propose a detection system to identify tactical user input requests.To evaluate the approach, we conducted a case study in a large global IT company. We observed that around 57% of the tickets have user input requests in the life cycle, causing user experienced resolution time to be almost twice as long as the measured service resolution time. The proposed preemptive system preempts the information needs with an average accuracy of 94-99% across five cross validations while traditional approaches such as logistic regression and naive Bayes have accuracy in the range of 50-60%. The detection system identifies around 15% of the total user input requests as tactical. Therefore, the proposed solution can efficiently bring down the number of user input requests and, hence, improve the user-experienced resolution time.

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Section: Application Of Recommendation Systems In Software Engineeringmentioning

confidence: 99%

Reducing user input requests to improve IT support ticket resolution process

et al. 2017

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“…Bug report deduplication is the querying of similar bug reports in order to cluster and group bug reports that report the same issue. Common tools in bug report deduplication are NLP Runeson et al (2007), machine-learning Bettenburg et al (2008); Sun et al (2010); ; Lazar et al (2014), information retrieval Sun et al (2011);Sureka and Jalote (2010), topic analysis Alipour (2013); ; Klein et al (2014). Zhang et al Zhang et al (2015) have applied typical bug-deduplication technology to StackOverflow duplicate question detection.…”

Section: Bug Report Deduplicationmentioning

confidence: 99%

“…The software systems that were mined range from cloud management systems, to mobile operating systems, to office suites. The projects were chosen because prior work used them (Android, Eclipse, Mozilla, and OpenOffice Aggarwal et al (2015); ; Sun et al (2011Sun et al ( , 2010) and due to availability of tools to mine their repositories. We also explicitly selected repositories with more than 50 duplicate bug pairs that were not used in prior duplicate bug report detection works.…”

Section: Datasetsmentioning

confidence: 99%

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Stopping duplicate bug reports before they start with Continuous Querying for bug reports

Hindle

2016

Preprint

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Bug deduplication is a hot topic in software engineering information retrieval research, but it is often not deployed. Typically to de-duplicate bug reports developers rely upon the search capabilities of the bug report software they employ, such as Bugzilla, Jira, or Github Issues. These search capabilities range from simple SQL string search to IR-based word indexing methods employed by search engines. Yet too often these searches do very little to stop the creation of duplicate bug reports. Some bug trackers have more than 10% of their bug reports marked as duplicate. Perhaps these bug tracker search engines are not enough? In this paper we propose a method of attempting to prevent duplicate bug reports before they start: continuous querying. That is as the bug reporter types in their bug report their text is used to query the bug database to find duplicate or related bug reports. This continuous querying allows the reporter to be alerted to duplicate bug reports as they report the bug, rather than formulating queries to find the duplicate bug report. Thus this work ushers in a new way of evaluating bug report deduplication techniques, as well as a new kind of bug deduplication task. We show that simple IR measures show some promise for addressing this problem but also that further research is needed to refine this novel process that is integrate-able into modern bug report systems.

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“…Unfortunately, in practice the triage process is mostly manual, and requires triagers with a good understanding of the project history. Consequently, researchers have sought ways to automatically suggest which new bug reports might be related to previously reported bugs using a variety of techniques, such as performing textual analysis of the free form text, looking for commonly recurring error messages, noting which API elements may be involved, and using machine learning techniques [13,14].…”

Section: The Problem Of Software Development Provenancementioning

confidence: 99%

Understanding software artifact provenance

Godfrey

2015

Science of Computer Programming

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In a well designed software system, units of related functionality are organized into modules and classes, which are in turn arranged into inheritance trees, package hierarchies, components, libraries, frameworks, and services. The trade-offs between simplicity versus flexibility and power are carefully considered, and interfaces are designed that expose the key functional properties of a component while hiding much of the complexity of the implementation details. However, over time the design integrity of a well-engineered system tends to decay as new features are added, as new quality attributes are emphasized, and as old architectural knowledge is lost when experienced development personnel shift to new jobs. Consequently, as developers and as users we often find ourselves looking at a piece of functionality or other design artifact and wondering, "Why is this here?" That is, we would like to examine the provenance of an artifact to understand its history and why it is where it is within the current design of the system.In this brief paper, we sketch some of the dimensions of the broad problem of extracting and reasoning about the provenance of software development artifacts. As a motivating example, we also describe some recent related work that uses hashing to quickly and accurately identify version information of embedded Java libraries.Keywords: Software artifact provenance, software analytics, code cloning Paul, it is easy to be impressed by your body of work at a distance. There are so many important and well-cited papers over many years, and the tools and techniques you and your group have contributed have had concrete and demonstrable impact on the research community. But when I spent my sabbatical at CWI, I learned an awful lot from you about running a research group, and getting the best out of talented people. You make it seem so easy and effortless, although I know it is neither. Your management style is low key and encouraging, yet the yield is so high. Perhaps most importantly, I have seen how the young researchers you supervise -many of whom go on to careers in industry -come away with justifiable pride in their efforts, an understanding of exactly how their work fits into the grand scheme of things, and a strong belief in the value of research itself. Surely this is the right way to build an ongoing, vibrant, and collegial community of scientists, engineers, and practitioners.

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A discriminative model approach for accurate duplicate bug report retrieval

Cited by 279 publications

References 19 publications

Reducing user input requests to improve IT support ticket resolution process

Reducing user input requests to improve IT support ticket resolution process

Stopping duplicate bug reports before they start with Continuous Querying for bug reports

Understanding software artifact provenance

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