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Improving Expert Estimation of Software Development Effort in Agile Contexts
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2018.
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 4
Keywords [en]
Expert effort estimation, Agile software development, Checklist
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-15870ISBN: 978-91-7295-350-5 (print)OAI: oai:DiVA.org:bth-15870DiVA, id: diva2:1181837
Public defence
2018-03-20, J1650, BTH Campus Gräsvik, Karlskrona, 10:00 (English)
Opponent
Supervisors
Available from: 2018-02-15 Created: 2018-02-09 Last updated: 2018-04-09Bibliographically approved
List of papers
1. Effort estimation in Agile Software Development: A systematic literature review
Open this publication in new window or tab >>Effort estimation in Agile Software Development: A systematic literature review
2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Ever since the emergence of agile methodologies in 2001, many software companies have shifted to Agile Software Development (ASD), and since then many studies have been conducted to investigate effort estimation within such context; however to date there is no single study that presents a detailed overview of the state of the art in effort estimation for ASD. Objectives: The aim of this study is to provide a detailed overview of the state of the art in the area of effort estimation in ASD. Method: To report the state of the art, we conducted a systematic literature review in accordance with the guidelines proposed in the evidence-based software engineering literature. Results: A total of 25 primary studies were selected; the main findings are: i) Subjective estimation techniques (e.g. expert judgment, planning poker, use case points estimation method) are the most frequently applied in an agile context; ii) Use case points and story points are the most frequently used size metrics respectively; iii) MMRE (Mean Magnitude of Relative Error) and MRE (Magnitude of Relative Error) are the most frequently used accuracy metrics; iv) team skills, prior experience and task size are cited as the three important cost drivers for effort estimation in ASD; and v) Extreme Programming (XP) and SCRUM are the only two agile methods that are identified in the primary studies. Conclusion: Subjective estimation techniques, e.g. expert judgment-based techniques, planning poker or the use case points method, are the one used the most in agile effort estimation studies. As for the size metrics, the ones that were used the most in the primary studies were story points and use case points. Several research gaps were identified, relating to the agile methods, size metrics and cost drivers, thus suggesting numerous possible avenues for future work

Place, publisher, year, edition, pages
Turin: ACM Press, 2014
Keywords
Agile software development, Effort estimation, Systematic literature review
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-6465 (URN)10.1145/2639490.2639503 (DOI)oai:bth.se:forskinfo5C3AE236B3C8427BC1257DBE00334806 (Local ID)978-1-4503-2898-2 (ISBN)oai:bth.se:forskinfo5C3AE236B3C8427BC1257DBE00334806 (Archive number)oai:bth.se:forskinfo5C3AE236B3C8427BC1257DBE00334806 (OAI)
Conference
International Conference on Predictive Models in Software Engineering (PROMISE), Turin
Available from: 2014-12-30 Created: 2014-12-30 Last updated: 2018-02-09Bibliographically approved
2. Effort estimation in agile software development: a survey on the state of the practice
Open this publication in new window or tab >>Effort estimation in agile software development: a survey on the state of the practice
2015 (English)In: Proceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering (EASE 2015), ACM Digital Library, 2015Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
ACM Digital Library, 2015
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-13753 (URN)978-1-4503-3350-4 (ISBN)
Conference
19th International Conference on Evaluation and Assessment in Software Engineering (EASE), Nanjing
Available from: 2017-01-13 Created: 2017-01-13 Last updated: 2018-02-09Bibliographically approved
3. Taxonomies in software engineering: A Systematic mapping study and a revised taxonomy development method
Open this publication in new window or tab >>Taxonomies in software engineering: A Systematic mapping study and a revised taxonomy development method
2017 (English)In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 85, p. 43-59Article in journal (Refereed) Published
Abstract [en]

Context: Software Engineering (SE) is an evolving discipline with new subareas being continuously developed and added. To structure and better understand the SE body of knowledge, taxonomies have been proposed in all SE knowledge areas. Objective: The objective of this paper is to characterize the state-of-the-art research on SE taxonomies. Method: A systematic mapping study was conducted, based on 270 primary studies. Results: An increasing number of SE taxonomies have been published since 2000 in a broad range of venues, including the top SE journals and conferences. The majority of taxonomies can be grouped into the following SWEBOI(knowledge areas: construction (19.55%), design (19.55%), requirements (15.50%) and maintenance (11.81%). Illustration (45.76%) is the most frequently used approach for taxonomy validation. Hierarchy (53.14%) and faceted analysis (39.48%) are the most frequently used classification structures. Most taxonomies rely on qualitative procedures to classify subject matter instances, but in most cases (86.53%) these procedures are not described in sufficient detail. The majority of the taxonomies (97%) target unique subject matters and many taxonomy-papers are cited frequently. Most SE taxonomies are designed in an ad-hoc way. To address this issue, we have revised an existing method for developing taxonomies in a more systematic way. Conclusion: There is a strong interest in taxonomies in SE, but few taxonomies are extended or revised. Taxonomy design decisions regarding the used classification structures, procedures and descriptive bases are usually not well described and motivated. (C) 2017 The Authors. Published by Elsevier B.V.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV, 2017
Keywords
Taxonomy, Classification, Software engineering, Systematic mapping study
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-15161 (URN)10.1016/j.infsof.2017.01.006 (DOI)000397553500003 ()
Available from: 2017-09-21 Created: 2017-09-21 Last updated: 2018-02-09Bibliographically approved
4. An Effort Estimation Taxonomy for Agile Software Development
Open this publication in new window or tab >>An Effort Estimation Taxonomy for Agile Software Development
2017 (English)In: International journal of software engineering and knowledge engineering, ISSN 0218-1940, Vol. 27, no 4, p. 641-674Article in journal (Refereed) Published
Abstract [en]

In Agile Software Development (ASD) effort estimation plays an important role during release and iteration planning. The state of the art and practice on effort estimation in ASD have been recently identified. However, this knowledge has not yet been organized. The aim of this study is twofold: (1) To organize the knowledge on effort estimation in ASD and (2) to use this organized knowledge to support practice and the future research on effort estimation in ASD. We applied a taxonomy design method to organize the identified knowledge as a taxonomy of effort estimation in ASD. The proposed taxonomy offers a faceted classification scheme to characterize estimation activities of agile projects. Our agile estimation taxonomy consists of four dimensions: estimation context, estimation technique, effort predictors and effort estimate. Each dimension in turn has several facets. We applied the taxonomy to characterize estimation activities of 10 agile projects identified from the literature to assess whether all important estimation-related aspects are reported. The results showed that studies do not report complete information related to estimation. The taxonomy was also used to characterize the estimation activities of four agile teams from three different software companies. The practitioners involved in the investigation found the taxonomy useful in characterizing and documenting the estimation sessions. © 2017 The Author(s).

Place, publisher, year, edition, pages
World Scientific Publishing Co. Pte Ltd, 2017
Keywords
agile software development, Effort estimation, taxonomy, Iterative methods, Software design, Taxonomies, Complete information, Effort estimates, Estimation techniques, Faceted Classification, Iteration planning, Software company, Software engineering
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-14472 (URN)10.1142/S0218194017500243 (DOI)000402062200006 ()2-s2.0-85019610980 (Scopus ID)
Available from: 2017-06-13 Created: 2017-06-13 Last updated: 2018-02-09Bibliographically approved
5. Effort Estimation in Large-Scale Software Development: An Industrial Case Study
Open this publication in new window or tab >>Effort Estimation in Large-Scale Software Development: An Industrial Case Study
2018 (English)In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 99, p. 21-40Article in journal (Refereed) Published
Abstract [en]

Context: Software projects frequently incur schedule and budget overruns. Planning and estimation are particularlychallenging in large and globally distributed projects. While software engineering researchers have beeninvestigating effort estimation for many years to help practitioners to improve their estimation processes, there is littleresearch about effort estimation in large-scale distributed agile projects.Objective: The main objective of this paper is three-fold: i) to identify how effort estimation is carried out in largescaledistributed agile projects; ii) to analyze the accuracy of the effort estimation processes in large-scale distributedagile projects; and iii) to identify the factors that impact the accuracy of effort estimates in large-scale distributed agileprojects.Method: We performed an exploratory longitudinal case study. The data collection was operationalized througharchival research and semi-structured interviews.Results: The main findings of this study are: 1) underestimation is the dominant trend in the studied case, 2) reestimationat the analysis stage improves the accuracy of the effort estimates, 3) requirements with large size/scopeincur larger effort overruns, 4) immature teams incur larger effort overruns, 5) requirements developed in multi-sitesettings incur larger effort overruns as compared to requirements developed in a collocated setting, and 6) requirementspriorities impact the accuracy of the effort estimates.Conclusion: Effort estimation is carried out at quotation and analysis stages in the studied case. It is a challengingtask involving coordination amongst many different stakeholders. Furthermore, lack of details and changes in requirements,immaturity of the newly on-boarded teams and the challenges associated with the large-scale add complexitiesin the effort estimation process.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
ffort estimation; Large-scale software development; Global and agile software development
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-15193 (URN)10.1016/j.infsof.2018.02.009 (DOI)000432767900003 ()
Funder
Knowledge Foundation
Available from: 2017-09-22 Created: 2017-09-22 Last updated: 2018-06-07Bibliographically approved
6. Developing and Using Checklists to Improve Software Effort Estimation: a Multi-Case Study
Open this publication in new window or tab >>Developing and Using Checklists to Improve Software Effort Estimation: a Multi-Case Study
(English)Manuscript (preprint) (Other academic)
Keywords
Effort estimation, checklist, agile software development
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-15871 (URN)
Available from: 2018-02-08 Created: 2018-02-08 Last updated: 2018-02-09Bibliographically approved

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