research-article

Open access

Identifying and describing information seeking tasks

Authors:

Chris Satterfield,

Gail C. MurphyAuthors Info & Claims

ASE '20: Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

Pages 797 - 808

https://doi.org/10.1145/3324884.3416537

Published: 27 January 2021 Publication History

Abstract

A software developer works on many tasks per day, frequently switching between these tasks back and forth. This constant churn of tasks makes it difficult for a developer to know the specifics of when they worked on what task, complicating task resumption, planning, retrospection, and reporting activities. In a first step towards an automated aid to this issue, we introduce a new approach to help identify the topic of work during an information seeking task --- one of the most common types of tasks that software developers face --- that is based on capturing the contents of the developer's active window at regular intervals and creating a vector representation of key information the developer viewed. To evaluate our approach, we created a data set with multiple developers working on the same set of six information seeking tasks that we also make available for other researchers to investigate similar approaches. Our analysis shows that our approach enables: 1) segments of a developer's work to be automatically associated with a task from a known set of tasks with average accuracy of 70.6%, and 2) a word cloud describing a segment of work that a developer can use to recognize a task with average accuracy of 67.9%.

References

[1]

Anonymous. 2020. Supplemental Material for the paper "Identifying and Describing a Software Developer's Tasks".

[2]

Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, and Ahmed E. Hassan. 2018. Inference of development activities from interaction with uninstrumented applications. Empirical Software Engineering 23, 3 (June 2018), 1313--1351.

Digital Library

[3]

Michael J. Coblenz, Andrew J. Ko, and Brad A. Myers. 2006. JASPER: An Eclipse Plug-in to Facilitate Software Maintenance Tasks. In Proceedings of the 2006 OOPSLA Workshop on Eclipse Technology Exchange. Association for Computing Machinery, 65--69.

Digital Library

[4]

Simon Corston-Oliver, Eric Ringger, Michael Gamon, and Richard Campbell. 2004. Task-focused summarization of email. In Text Summarization Branches Out. Association of Computational Linguistics, 43--50.

[5]

K. Damevski, H. Chen, D. C. Shepherd, N. A. Kraft, and L. Pollock. 2018. Predicting Future Developer Behavior in the IDE Using Topic Models. IEEE Transactions on Software Engineering 44, 11 (2018), 1100--1111.

[6]

Márcio Kuroki Gonçalves, Cleidson RB de Souza, and Victor M Gonzalez. 2011. Collaboration, Information Seeking and Communication: An Observational Study of Software Developers' Work Practices. J. UCS 17, 14 (2011), 1913--1930.

[7]

Victor M González and Gloria Mark. 2004. "Constant, Constant, Multi-tasking Craziness": Managing Multiple Working Spheres. In Proceedings of the 2004 Conference on Human Factors in Computing Systems, CHI 2004. 113--120.

Digital Library

[8]

Thomas Gottron. 2009. Document Word Clouds: Visualising Web Documents as Tag Clouds to Aid Users in Relevance Decisions. In Research and Advanced Technology for Digital Libraries, Maristella Agosti, José Borbinha, Sarantos Kapidakis, Christos Papatheodorou, and Giannis Tsakonas (Eds.). Springer Berlin Heidelberg, 94--105.

[9]

Qiao Huang, Xin Xia, David Lo, and Gail C. Murphy. 2018. Automating Intention Mining. IEEE Transactions on Software Engineering (2018), 1--1. Early access.

[10]

ImageMagick. 2020. ImageMagick. https://imagemagick.org/index.php. [Accessed March 5, 2020].

[11]

Tom Kenter, Alexey Borisov, and Maarten De Rijke. 2016. Siamese cbow: Optimizing word embeddings for sentence representations. arXiv preprint arXiv:1606.04640 (2016).

[12]

Mik Kersten and Gail C. Murphy. 2006. Using Task Context to Improve Programmer Productivity. In Proceedings of the 14th ACM SIGSOFT International Symposium on Foundations of Software Engineering (SIGSOFT '06/FSE-14). ACM, 1--11.

Digital Library

[13]

Katja Kevic and Thomas Fritz. 2017. Towards Activity-Aware Tool Support for Change Tasks. In 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, 171--182.

[14]

Saskia Koldijk, Mark van Staalduinen, Mark Neerincx, and Wessel Kraaij. 2012. Real-time task recognition based on knowledge workers' computer activities. In Proceedings of the 30th European Conference on Cognitive Ergonomics (ECCE '12). Association for Computing Machinery, 152--159.

Digital Library

[15]

Mary L. McHugh. 2012. Interrater reliability: the kappa statistic. Biochemia Medica 22, 3 (Oct. 2012), 276--282.

[16]

André Meyer, Gail C Murphy, Thomas Zimmermann, and Thomas Fritz. 2017. Design Recommendations for Self-Monitoring in the Workplace: Studies in Software Development. PACM on Human-Computer Interaction 1, CSCW (2017), 1--24.

Digital Library

[17]

A. N. Meyer, L. E. Barton, G. C. Murphy, T. Zimmermann, and T. Fritz. 2017. The Work Life of Developers: Activities, Switches and Perceived Productivity. IEEE Transactions on Software Engineering 43, 12 (2017), 1178--1193.

Digital Library

[18]

A. N. Meyer, C. Satterfield, M. Züger, K. Kevic, G. C. Murphy, T. Zimmermann, and T. Fritz. 2020. Detecting Developers' Task Switches and Types. IEEE Transactions on Software Engineering (2020). Early access.

[19]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111--3119.

[20]

Allen E. Milewski. 2007. Global and task effects in information-seeking among software engineers. Empir. Softw. Eng. 12, 3 (2007), 311--326.

Digital Library

[21]

Hamid Turab Mirza, Ling Chen, Gencai Chen, Ibrar Hussain, and Xufeng He. 2011. Switch detector: an activity spotting system for desktop. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM '11). Association for Computing Machinery, 2285--2288.

Digital Library

[22]

Hamid Turab Mirza, Ling Chen, Ibrar Hussain, Abdul Majid, and Gencai Chen. 2015. A Study on Automatic Classification of Users' Desktop Interactions. Cybernetics and Systems 46, 5 (2015), 320--341.

Digital Library

[23]

Mozilla. 2020. Bugzilla. https://bugzilla.mozilla.org/home. [Accessed August 31, 2020].

[24]

NLTK. 2020. Natural Language Toolkit (NLTK). https://www.nltk.org/. [Accessed March 5, 2020].

[25]

Nuria Oliver, Greg Smith, Chintan Thakkar, and Arun C Surendran. 2006. SWISH: semantic analysis of window titles and switching history. In Proceedings of the 11th International Conference on Intelligent User Interfaces. 194--201.

Digital Library

[26]

Chris Parnin and Robert DeLine. 2010. Evaluating cues for resuming interrupted programming tasks. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '10). Association for Computing Machinery, 93--102.

Digital Library

[27]

Chris Parnin and Spencer Rugaber. 2011. Resumption strategies for interrupted programming tasks. Software Quality Journal 19, 1 (March 2011), 5--34.

Digital Library

[28]

Luca Ponzanelli, Gabriele Bavota, Andrea Mocci, Massimiliano Di Penta, Rocco Oliveto, Barbara Russo, Sonia Haiduc, and Michele Lanza. 2016. CodeTube: Extracting Relevant Fragments from Software Development Video Tutorials. In 2016 IEEE/ACM 38th International Conference on Software Engineering Companion (ICSE-C). 645--648.

Digital Library

[29]

RAKE. 2020. Python implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK. https://pypi.org/project/rake-nltk/. [Accessed March 5, 2020].

[30]

Stuart Rose, Dave Engel, Nick Cramer, and Wendy Cowley. 2010. Automatic Keyword Extraction from Individual Documents. In Text Mining: Applications and Theory. 1--20. Journal Abbreviation: Text Mining: Applications and Theory.

[31]

Jianqiang Shen, Werner Geyer, Michael Muller, Casey Dugan, Beth Brownholtz, and David R Millen. 2008. Automatically finding and recommending resources to support knowledge workers' activities. In Proceedings of the 13th International Conference on Intelligent User Interfaces (IUI '08). Association for Computing Machinery, 207--216.

Digital Library

[32]

Jianqiang Shen, Lida Li, and Thomas G. Dietterich. 2007. Real-time detection of task switches of desktop users. In Proceedings of the 20th International Joint Conference on Artifical Intelligence (IJCAI'07). Morgan Kaufmann Publishers Inc., 2868--2873.

[33]

Andrea Di Sorbo, Sebastiano Panichella, Corrado A. Visaggio, Massimiliano Di Penta, Gerardo Canfora, and Harald C. Gall. 2015. Development Emails Content Analyzer: Intention Mining in Developer Discussions (T). In 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE). 12--23.

Digital Library

[34]

Tesseract. 2020. Tesseract Open Source OCR Engine. https://github.com/tesseractocr. [Accessed March 5, 2020].

Cited By

R. Yousefi ZVuong TAlGhossein MRuotsalo TJaccuci GKaski S(2024)Entity Footprinting: Modeling Contextual User States via Digital Activity MonitoringACM Transactions on Interactive Intelligent Systems10.1145/364389314:2(1-27)Online publication date: 5-Feb-2024
https://dl.acm.org/doi/10.1145/3643893
Krüger JÇalıklı GBershadskyy DOtto SZabel SHeyer R(2024)Guidelines for using financial incentives in software-engineering experimentationEmpirical Software Engineering10.1007/s10664-024-10517-w29:5Online publication date: 10-Aug-2024
https://dl.acm.org/doi/10.1007/s10664-024-10517-w
Bakhshizadeh MJilek CSchröder MMaus HDengel A(2024)Data Collection of Real-Life Knowledge Work in Context: The RLKWiC DatasetInformation Management10.1007/978-3-031-64359-0_22(277-290)Online publication date: 18-Jul-2024
https://doi.org/10.1007/978-3-031-64359-0_22

Index Terms

Identifying and describing information seeking tasks
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

Recommendations

A faceted approach to conceptualizing tasks in information seeking

The nature of the task that leads a person to engage in information interaction, as well as of information seeking and searching tasks, have been shown to influence individuals' information behavior. Classifying tasks in a domain has been viewed as a ...
Natural Language Processing for Productivity Metrics for Software Development Profiling in Enterprise Applications
AICCC '18: Proceedings of the 2018 Artificial Intelligence and Cloud Computing Conference

In this paper, we utilize ontology-based information extraction for semantic analysis and terminology linking from a corpus of software requirement specification documents from 400 enterprise-level software development projects. The purpose for this ...
Task complexity and information searching in administrative tasks revisited
IIIX '12: Proceedings of the 4th Information Interaction in Context Symposium

In task-based information searching, the task at hand is a central factor affecting information search. Task complexity, in particular, has been discovered to affect searching. In the present study, we shadowed the tasks of seven people working in city ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ASE '20: Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

December 2020

1449 pages

ISBN:9781450367684

DOI:10.1145/3324884

General Chair:
John Grundy,
Program Chairs:
Claire Le Goues,
David Lo

Copyright © 2020 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

In-Cooperation

IEEE CS

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 January 2021

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

NSERC
ABB Inc.

Conference

ASE '20

Sponsor:

ASE '20: 35th IEEE/ACM International Conference on Automated Software Engineering

December 21 - 25, 2020

Virtual Event, Australia

Acceptance Rates

Overall Acceptance Rate 82 of 337 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
391
Total Downloads

Downloads (Last 12 months)103
Downloads (Last 6 weeks)17

Reflects downloads up to 25 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

R. Yousefi ZVuong TAlGhossein MRuotsalo TJaccuci GKaski S(2024)Entity Footprinting: Modeling Contextual User States via Digital Activity MonitoringACM Transactions on Interactive Intelligent Systems10.1145/364389314:2(1-27)Online publication date: 5-Feb-2024
https://dl.acm.org/doi/10.1145/3643893
Krüger JÇalıklı GBershadskyy DOtto SZabel SHeyer R(2024)Guidelines for using financial incentives in software-engineering experimentationEmpirical Software Engineering10.1007/s10664-024-10517-w29:5Online publication date: 10-Aug-2024
https://dl.acm.org/doi/10.1007/s10664-024-10517-w
Bakhshizadeh MJilek CSchröder MMaus HDengel A(2024)Data Collection of Real-Life Knowledge Work in Context: The RLKWiC DatasetInformation Management10.1007/978-3-031-64359-0_22(277-290)Online publication date: 18-Jul-2024
https://doi.org/10.1007/978-3-031-64359-0_22

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Figures

Tables

Media

View Table of Conten