Replication Package for "Understanding Practitioners' Reasoning and Requirements for Efficient Tool Support in Technical Debt Management"
Creators: name: Biazotto, João Paulo
name: Feitosa, Daniel
name: Avgeriou, Paris
name: Nakagawa, Elisa Yumi
Publisher: name: Zenodo
Year: 2025

description: Context: Maintaining software projects over the long term requires controlling the accumulation of technical debt (TD). However, the time and cost associated with technical debt management (TDM) are often high, hindering practitioners from performing TDM tasks. Using tools for TDM has the potential to reduce the effort involved. Despite this, the adoption of such tools remains low, indicating a need for more efficient tool support.

Objective: This study aims to understand practitioners' perspectives on tool support for TDM, specifically regarding their selection and use of these tools. Additionally, we seek to identify potential requirements that could be implemented into existing or new TDM tools.

Methods: We surveyed practitioners, and received 103 answers, from which 100 valid answers were analyzed using both thematic analysis and descriptive statistics.

Results: Practitioners' decision-making processes regarding adopting tools are primarily driven by ten main concerns identified from practitioners' responses (e.g., the load of information provided by tools). Additionally, we elicited 46 requirements and classified them into two main categories ("Information to be provided" and "Tool Usage").

Conclusion: Practitioners aim to maintain control over tool execution and outputs. Therefore, our study highlights the necessity of human-centered approaches for TDM automation, i.e., not only tools are essential, but the interaction between tools and practitioners is critical for a more efficient TDM.



Structure of the replication package:


The replication package includes the datasets (for answers, codes, and indicators), the scripts for data analysis, and all the figures used in the manuscript.



├── data|   ├── questionnaires|   |   ├── 0-invitation-letters-email.pdf|   |   ├── 1-invitation-letters-social-media.pdf|   |   ├── s1.pdf|   |   ├── s2.pdf|   |   ├── s3.pdf|   |   ├── s4.pdf|   |   └── s5.pdf|   ├── scenario-designs|   |   ├── s1-c4.png|   |   ├── s1-mockup.JPG|   |   ├── s2-c4.png|   |   ├── s2-mockup.png|   |   ├── s3-c4.png|   |   ├── s3-mockup.png|   |   ├── s4-c4.png|   |   ├── s4-mockup.JPG|   |   ├── s5-c4.png|   |   └── s5-mockup.png|   ├── atlas-bundle.atlasti|   ├── codebook.xlsx|   ├── pre-coding.xlsx|   ├── quotations.csv|   ├── requirements.csv|   ├── respondents.csv|   ├── s1-answers.xlsx|   ├── s2-answers.xlsx|   ├── s3-answers.xlsx|   ├── s4-answers.xlsx|   └── s5-answers.xlsx├── figures│   ├── data-analysis-rq1.pdf│   ├── data-analysis-rq2.pdf│   ├── overview-research-phases.pdf│   ├── requirements-categories.pdf│   ├── requirements-categorization-25010.pdf│   ├── requirements-template.pdf│   ├── s2-mockup.png│   ├── scenario-development-process.pdf|   └── theory.pdf├── scripts│   ├── helpers.py│   ├── highlight-codes.py│   ├── rq0-demographics.ipynb│   ├── rq1-concerns.ipynb│   ├── rq2-requirements.ipynb│   └── rqn-correlation-between-requirements-and-profiles.ipynb├── docker-compose.yaml├── Dockerfile├── env.yaml├── LICENSE.txt└── README.md


 




Description of each variable in codebook.xlsx





variable name
description




code
code used to represent indicators






 

Description of each variable in pre-coding.xlsx





variable name
description




scenario
the scenario from which the answer was collected


question
the question from a scenario


sample
a sample of answers related to the questions


coder 1-3
the codes assigned by each coder to the answer


agreement
codes that were agreed by at least two coders


single incident
codes that were assigned by a single coder






 

Description of each variable in quotations.csv





variable name
description




id
the identifier for a quotation, generated by Atlas.ti


document
the identifier of a response in the survey, indicated the scenario and the respondent (e.g., s1_r2)


content
the text of a quoatation


codes
a list of codes associated with the quotations (e.g., 1-generic td label is not useful for prioritization)






 

Description of each variable in requirements.csv





variable name
description


id
the identifier of a requirement, based on the category of a requirement (e.g., INF01)


scenario
the scenario from which the requirements was extracted


system
indicates the tool presented in the scenario from which the requirement was extracted (e.g., code review bot)


verb
the verb that is used in the requirement, i.e., will


process
refers to the core of each requirement, i.e., the functionality that it specifies (e.g., present, enable, notify, etc.)


object
what and where a process will be executed (e.g., what information will be presented in a dashboard)


category
the category of a requirement (e.g., information to be provided)


complete-requirement
the requirements phrased with all fields (e.g., process and object)


code
the code used to elicit the requirement


td-types
the type of TD that is related to the requirements (e.g., code)


tdm-activities
the TDM activities that are supported by the requirement (e.g., repayment)


qa-25010
the quality attribute, as per ISO 25010, that is impacted by the requirement (e.g., operability)






 

Description of each variable in respondents.csv





variable name
description




id
the identifier of a respondent, based on the scenario (e.g., s2_r18)


scenario
the scenario in answered by the respondent (e.g., s4)


education
the education level of a respondent (e.g., Master)


experience
the time of experience of a respondent (e.g., 1-5 years)


role
the current role of a respondent (e.g., software developer)


valid
a boolean that indicate if the answer was considered for the data analysis






 

Description of each variable in sX-answers.xlsx (i.e., files that were uploaded to Atlas.ti)





variable name
description




!id
the identifier of a respondent, based on the scenario (e.g., s2_r18)


:scenario
the scenario in answered by the respondent (e.g., s4)


:education
the education level of a respondent (e.g., Master)


:experience
the time of experience of a respondent (e.g., 1-5 years)


:role
the current role of a respondent (e.g., software developer)


:usefulness
the preceived usefulness of a scenario


sX_qX::XXXXX
variables with this format represents answers for a certain question (q) in a scenario (s), e.g., s1_q1::Would such a label be useful to prioritize the issues that should be addressed? Please justify your answer.






 

Running the data analysis




Install Docker and docker-compose

Open a terminal (e.g., Windows Powershell) and navigate to the folder where replication package is saved

Run docker compose build

After the environment is installed, run docker compose up

Open a browser and access localhost:8888/lab

Run the analysis contained in each of the scripts files, which are saved in the folder scripts. Those scripts are organized by research question (e.g., rq2-requirements.ipynb).