High-level software requirements and iteration changes : a predictive model
Blincoe, Kelly ; Dehghan, Ali ; Salaou, Abdoul Djawadou ; Neal, Adam ; Linaker, Johan LU
and Damian, Daniela
(2019)
In Empirical Software Engineering
24(3).
p.1610-1648
- Abstract
Knowing whether a software feature will be completed in its planned iteration can help with release planning decisions. However, existing research has focused on predictions of only low-level software tasks, like bug fixes. In this paper, we describe a mixed-method empirical study on three large IBM projects. We investigated the types of iteration changes that occur. We show that up to 54% of high-level requirements do not make their planned iteration. Requirements are most often pushed out to the next iteration, but high-level requirements are also commonly moved to the next minor or major release or returned to the product or release backlog. We developed and evaluated a model that uses machine learning to predict if a high-level... (More)
Knowing whether a software feature will be completed in its planned iteration can help with release planning decisions. However, existing research has focused on predictions of only low-level software tasks, like bug fixes. In this paper, we describe a mixed-method empirical study on three large IBM projects. We investigated the types of iteration changes that occur. We show that up to 54% of high-level requirements do not make their planned iteration. Requirements are most often pushed out to the next iteration, but high-level requirements are also commonly moved to the next minor or major release or returned to the product or release backlog. We developed and evaluated a model that uses machine learning to predict if a high-level requirement will be completed within its planned iteration. The model includes 29 features that were engineered based on prior work, interviews with IBM developers, and domain knowledge. Predictions were made at four different stages of the requirement lifetime. Our model is able to achieve up to 100% precision. We ranked the importance of our model features and found that some features are highly dependent on project and prediction stage. However, some features (e.g., the time remaining in the iteration and creator of the requirement) emerge as important across all projects and stages. We conclude with a discussion on future research directions.
(Less)
- author
- Blincoe, Kelly
; Dehghan, Ali
; Salaou, Abdoul Djawadou
; Neal, Adam
; Linaker, Johan
LU
and Damian, Daniela
- organization
- publishing date
- 2019
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Completion prediction, Machine learning, Mining software repositories, Release planning, Software requirements
- in
- Empirical Software Engineering
- volume
- 24
- issue
- 3
- pages
- 1610 - 1648
- publisher
- Springer
- external identifiers
-
- scopus:85055479920
- ISSN
- 1382-3256
- DOI
- 10.1007/s10664-018-9656-z
- language
- English
- LU publication?
- yes
- id
- 588e6a36-80a4-4161-ac36-319cdb5540c6
- date added to LUP
- 2018-11-22 09:21:17
- date last changed
- 2022-07-12 03:50:34
@article{588e6a36-80a4-4161-ac36-319cdb5540c6,
abstract = {{<p>Knowing whether a software feature will be completed in its planned iteration can help with release planning decisions. However, existing research has focused on predictions of only low-level software tasks, like bug fixes. In this paper, we describe a mixed-method empirical study on three large IBM projects. We investigated the types of iteration changes that occur. We show that up to 54% of high-level requirements do not make their planned iteration. Requirements are most often pushed out to the next iteration, but high-level requirements are also commonly moved to the next minor or major release or returned to the product or release backlog. We developed and evaluated a model that uses machine learning to predict if a high-level requirement will be completed within its planned iteration. The model includes 29 features that were engineered based on prior work, interviews with IBM developers, and domain knowledge. Predictions were made at four different stages of the requirement lifetime. Our model is able to achieve up to 100% precision. We ranked the importance of our model features and found that some features are highly dependent on project and prediction stage. However, some features (e.g., the time remaining in the iteration and creator of the requirement) emerge as important across all projects and stages. We conclude with a discussion on future research directions.</p>}},
author = {{Blincoe, Kelly and Dehghan, Ali and Salaou, Abdoul Djawadou and Neal, Adam and Linaker, Johan and Damian, Daniela}},
issn = {{1382-3256}},
keywords = {{Completion prediction; Machine learning; Mining software repositories; Release planning; Software requirements}},
language = {{eng}},
number = {{3}},
pages = {{1610--1648}},
publisher = {{Springer}},
series = {{Empirical Software Engineering}},
title = {{High-level software requirements and iteration changes : a predictive model}},
url = {{http://dx.doi.org/10.1007/s10664-018-9656-z}},
doi = {{10.1007/s10664-018-9656-z}},
volume = {{24}},
year = {{2019}},
}