Evidence map›Paper›PMID 40378372›Full record

ArticlePloS one2025

Applying a system dynamics approach for decision-making in software testing projects.

Wang Li, Chih-Chiang Fang

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Wang LiSchool of Computer Science and Software, Zhaoqing University, Zhaoqing, China.
Chih-Chiang FangSchool of Computer Science and Software, Zhaoqing University, Zhaoqing, China.ORCID https://orcid.org/0009-0008-7737-4701

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Enhancing software quality remains a main objective for software developers and engineers, with a specific emphasis on improving software stability to increase user satisfaction. Developers must balance rigorous software testing with tight schedules and budgets. This often forces them to choose between quality and cost. Traditional approaches rely on software reliability growth models but are often too complex and impractical for testing complex software environments. Addressing this issue, our study introduces a system dynamics approach to develop a more adaptable software reliability growth model. This model is specifically designed to handle the complexities of modern software testing scenarios. By utilizing a system dynamics model and a set of defined rules, we can effectively simulate and illustrate the impacts of testing and debugging processes on the growth of software reliability. This method simplifies the complex mathematical derivations that are commonly associated with traditional models, making it more accessible for real-world applications. The key innovation of our approach lies in its ability to create a dynamic and interactive model that captures the various elements influencing software reliability. This includes factors such as resource allocation, testing efficiency, error detection rates, and the feedback loops among these elements. By simulating different scenarios, software developers and project managers can gain deeper insights into the impact of their decisions on software quality and testing efficiency. This can provide valuable insights for decision-making and strategy formulation in software development and quality assurance.

Indexed as

Decision MakingSoftwareHumansModels, TheoreticalReproducibility of Results

Identifiers

PMID40378372
PMCPMC12084065

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.