| 1. |
- Duszkiewicz, Aleksander G., et al.
(författare)
-
Leveraging Historical Data to Support User Story Estimation
- 2023
-
Ingår i: Product-Focused Software Process Improvement. - : Springer. - 9783031492655 ; , s. 284-300
-
Konferensbidrag (refereegranskat)abstract
- Accurate and reliable effort and cost estimation are still challenging for agile teams in the industry. It is argued that leveraging historical data regarding the actual time spent on similar past projects could be very helpful to support such an activity before companies embark upon a new project. In this paper, we investigate to what extent user story information retrieved from past projects can help developers estimate the effort needed to develop new similar projects. In close collaboration with a software development company, we applied design science and action research principles to develop and evaluate a tool that employs Natural Language Processing (NLP) algorithms to find past similar user stories and retrieve the actual time spent on them. The tool was then used to estimate a real project that was about to start in the company. A focus group with a team of six developers was conducted to evaluate the tool’s efficacy in estimating similar projects. The results of the focus group with the developers revealed that the tool has the potential to complement the existing estimation process and help different interested parties in the company. Our results contribute both towards a new tool-supported approach to help user story estimation based on historical data and with our lessons learned on why, when, and where such a tool and the estimations provided may play a role in agile projects in the industry.
|
|
| 2. |
- Mårtensson, Torvald, et al.
(författare)
-
The Testing Hopscotch Model - Six Complementary Profiles Replacing the Perfect All-Round Tester
- 2024
-
Ingår i: PRODUCT-FOCUSED SOFTWARE PROCESS IMPROVEMENT, PROFES 2023, PT I. - : SPRINGER INTERNATIONAL PUBLISHING AG. - 9783031492655 - 9783031492662 ; , s. 495-510
-
Konferensbidrag (refereegranskat)abstract
- Contrasting the idea of a team with all-round testers, the Testing Hopscotch model includes six complementary profiles, tailored for different types of testing. The model is based on 60 interviews and three focus groups with 22 participants. The validation of the Testing Hopscotch model included ten validation workshops with 58 participants from six companies developing large-scale and complex software systems. The validation showed how the model provided valuable insights and promoted good discussions, helping companies identify what they need to do in order to improve testing in each individual case. The results from the validation workshops were confirmed at a cross-company workshop with 33 participants from seven companies and six universities. Based on the diverse nature of the seven companies involved in the study, it is reasonable to expect that the Testing Hopscotch model is relevant to a large segment of the software industry at large. The validation of the Testing Hopscotch model showed that the model is novel, actionable and useful in practice, helping companies identify what they need to do to improve testing in their organization.
|
|
| 3. |
- Salin, Hannes
(författare)
-
A Stochastic Approach Based on Rational Decision-Making for Analyzing Software Engineering Project Status
- 2023
-
Ingår i: Product-Focused Software Process Improvement. - : Springer. - 9783031492662 - 9783031492655 ; , s. 175-182
-
Konferensbidrag (refereegranskat)abstract
- This study presents a novel approach to project status prediction in software engineering, based on unobservable states of decision-making processes, utilizing Hidden Markov Models (HMMs). By establishing HMM structures and leveraging the Rational Decision Making model (RDM), we encoded underlying project conditions; observed project data from a software engineering organization were utilized to estimate model parameters via the Baum-Welch algorithm. The developed HMMs, four project-specific models, were subsequently tested with empirical data, demonstrating their predictive potential. However, a generalized, aggregated model did not show any sufficient accuracy. Model development and experiments were made in Python. Our approach presents preliminary work and a pathway for understanding and forecasting project dynamics in software development environments.
|
|
