Assisting engineers in switching artifacts by using task semantic and interaction history

Maalej, Walid; Sahm, Alexander

doi:10.1145/1808920.1808935

Cited by 16 publications

(6 citation statements)

References 18 publications

(19 reference statements)

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“…They evaluated various change prediction methods and showed that predictions based on the most recently changed files are the most accurate [19]. Maalej et al recorded the interaction histories of various development tools and used these to recommend the development tool that the user should use next [20]. Roehm et al collected code change, Web search, and compile error histories, and produced a representation of the steps taken by a developer when resolving problems by applying the hidden Malkov model [21].…”

Section: Methods Of Mining Interaction Historymentioning

confidence: 99%

A Change Guide Method Based on Developers' Interaction and Past Recommendation

Yamamori

Kobayashi²

2016

Modelling, Identification and Control / 834: Parallel and Distributed Computing and Networks / 835: Software Engineering

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In this paper, we propose a change guide method based on the past developers' activity that consists of read and write access records of artifacts. In our proposed method, we calculate candidates of next change recommendation considering the history of its recommendations. We define "cumulative likelihood" to enable the method to recommend the appropriate candidates when a change propagates more than one code elements. A case study using interaction history logs from 15 participants showed the improvement of the accuracy of the method-level change recommendation.

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Section: Methods Of Mining Interaction Historymentioning

confidence: 99%

A Change Guide Method Based on Developers' Interaction and Past Recommendation

Yamamori

Kobayashi²

2016

Modelling, Identification and Control / 834: Parallel and Distributed Computing and Networks / 835: Software Engineering

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“…The prerequisite to using a personal work context is its proper extraction. According to Maalej and Sahm [17], software engineers spend only about half of their time on code creation. Software engineers also use on average five tools for a single task and read or change different artifacts, like source code files, bug reports, or diagrams.…”

Section: Extracting the Personal Work Context From Developer Activitiesmentioning

confidence: 99%

Constraining peripheral perception in instant messaging during software development by continuous work context extraction

van

Vranić

2022

Univ Access Inf Soc

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Colocated software development teams benefit from natural work context building, which occurs mainly thanks to the team members, virtually, being forced to listen to what others are talking about. They absorb the information not by directing their attention to the communication, but by being exposed to it and perceiving it peripherally. The same effect of peripheral perception can be enforced with instant messaging, which is a predominant way of communication in distributed teams. However, forcing team members to observe too many and mostly unrelated message notifications can be distracting and causing unnecessary work interruption. This paper presents an approach and tool that ensure peripheral perception in instant messaging constrained by a continuously extracted work context. This is achieved by maintaining a personal work context from developer activities and using this context to filter instant messages to be displayed. A four-week experiment carried out with one of the teams of seven members in the Team Project course at our university indicates that message filtering based on continuous work context extraction performs better over common channel based filtering (as available in Slack). More precisely, message filtering based on continuous work context extraction decreases work interruption and distraction.

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“…Software development environments are starting to integrate RSs to assist developers in various software engineering activities, from reusing code to effective bug reports [23]. Examples of recommended items in these systems are method calls that can be useful in a certain context [33], software components that may be reused in a given situation [17], and required software artefacts [16].…”

Section: Recommender Systemsmentioning

confidence: 99%

Towards automating the construction of recommender systems for low-code development platforms

Almonte

Cantador

Guerra

et al. 2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

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Low-code development platforms allow users with a low technical background to build complete software solutions, typically by means of graphical user interfaces, diagrams or declarative languages. In these platforms, recommender systems play an important role as they can provide users with relevant, personalised suggestions generated according to previously developed software solutions. However, developing recommender systems requires a high investment of time as it implies the selection and implementation of a suitable recommendation method, its configuration for the problem and domain at hand, and its evaluation to assess the accuracy of its recommendations.To alleviate these problems, in this paper, we present the first steps towards a generic model-driven framework capable of generating ad-hoc, task-oriented recommender systems for their integration on low-code platforms. As a proof of concept, we present some preliminary results obtained from an offline evaluation of our framework on three datasets of class diagrams. The results show that the proposed framework is capable of providing relevant recommendations in the given context. CCS CONCEPTS• Software and its engineering → Development frameworks and environments.

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Assisting engineers in switching artifacts by using task semantic and interaction history

Cited by 16 publications

References 18 publications

A Change Guide Method Based on Developers' Interaction and Past Recommendation

A Change Guide Method Based on Developers' Interaction and Past Recommendation

Constraining peripheral perception in instant messaging during software development by continuous work context extraction

Towards automating the construction of recommender systems for low-code development platforms

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