An exploratory study on the influence of developers in technical debt

Alfayez, Reem; Behnamghader, Pooyan; Srisopha, Kamonphop; Boehm, Barry

doi:10.1145/3194164.3194165

Cited by 21 publications

(40 citation statements)

References 18 publications

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“…We expect that the developers that write more code are the ones that introduce more TD. However, it can also be expected that the developers that are more familiar with the project produce proportionality less TD [5]. That is why we formulate next RQ.…”

Section: A Research Questionsmentioning

confidence: 97%

“…Alfayez et al [5] assessed in 38 Apache projects how developer factors such as the developer's frequency of commits, her seniority, and the size of the time lapses between her commits are related to TD. The research results show that the introduction of TD by developers is distributed unequally, with some developers adding more TD than others.…”

Section: Related Workmentioning

confidence: 99%

“…We use Spearman's correlation test to analyze the relationship between developer factors and code quality. We used it because it is less sensitive to the outliers that exist due to the inherent heterogeneity of the data that comes from different open source projects, as it is reported in another studies [5].…”

Section: A Conclusion Validitymentioning

confidence: 99%

“…Particularly, developers have an essential role in software quality [3]. In this regard, studies have shown that developer factors like experience, involvement, compromise, and communication have a direct influence on code quality [4] [5].…”

Section: Introductionmentioning

confidence: 99%

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Influence of Developer Factors on Code Quality: A Data Study

Salamea

Farré

2019

2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C)

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Automatic source-code inspection tools help to assess, monitor and improve code quality. Since these tools only examine the software project's codebase, they overlook other possible factors that may impact code quality and the assessment of the technical debt (TD). Our initial hypothesis is that human factors associated with the software developers, like coding expertise, communication skills, and experience in the project have some measurable impact on the code quality. In this exploratory study, we test this hypothesis on two large open source repositories, using TD as a code quality metric and the data that may be inferred from the version control systems. The preliminary results of our statistical analysis suggest that the level of participation of the developers and their experience in the project have a positive correlation with the amount of TD that they introduce. On the contrary, communication skills have barely any impact on TD. Index Terms-code quality, technical debt, human factors, data mining.

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Section: A Research Questionsmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Section: A Conclusion Validitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Influence of Developer Factors on Code Quality: A Data Study

Salamea

Farré

2019

2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C)

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“…It enables analysis of the conflicts and synergies between different quality attributes and the difference between developers in terms of their impact on software quality. Our integrated tool‐based approach has been documented in multiple research publications empowering their empirical studies and is used by a major governmental entity.…”

Section: Squaad Toolsetmentioning

confidence: 99%

Anticipatory development processes for reducing total ownership costs and schedules

Boehm

Behnamghader

2019

Systems Engineering

Self Cite

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Many systems and software processes overfocus on getting a project and product from an initial set of requirements to an Initial Operational Capability (IOC). Examples are most waterfall and V models. Projects following such processes may pass acceptance tests for functionality and performance, but may leave the product with serious maintainability shortfalls. Many agile processes focus on users' initial usage priorities, but often make development commitments for earlier needs that are incompatible with achieving later critical needs (eg, security, safety). Incremental development process models can do better, but often later increments may find that the earlier increments have not prepared them for ease of modification and repair. Besides increasing Total Ownership Costs (TOCs), long mean times to repair result in long downtimes, which can be critical to an organization's income and reputation. Furthermore, many of these shortfalls take the form of Technical Debt (TD), in that the later they are fixed, the more slow and expensive will be the fixes. This paper summarizes three process frameworks and tools providing more anticipatory ways to improve systems and software maintainability and life‐cycle cost‐effectiveness. The first framework is an Opportunity Tree for identifying and anticipating such ways. The second framework, Software Quality Understanding by Analysis of Abundant Data (SQUAAD), is a toolset for tracking a software project's incremental code commits, and analyzing and visualizing each commit's incremental and cumulative TD. The third framework is a Software/Systems Maintenance Readiness Framework (SMRF), that identifies needed maintenance readiness levels at development decision reviews, similar to the Technology Readiness Levels (TRLs) framework.

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Analyzing the Correlation Between Toxic Comments and Code Quality

Sayago‐Heredia,

Sailema,

Pérez‐Castillo

et al. 2024

J Software Evolu Process

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Software development has a relevant human side, and this could, for example, imply that developers' feelings have an impact on certain aspects of software development such as quality, productivity, or performance. This paper explores the effects of toxic emotions on code quality and presents the SentiQ tool, which gathers and analyzes sentiments from commit messages (obtained from GitHub) and code quality measures (obtained from SonarQube). The SentiQ tool we proposed performs a sentiment analysis (based on natural language processing techniques) and relates the results to the code quality measures. The datasets extracted are then used as the basis on which to conduct a preliminary case study, which demonstrates that there is a relationship between toxic comments and code quality that may affect the quality of the whole software project. This has resulted in the drafting of a predictive model to validate the correlation of the impact of toxic comments on code quality. The main implication of this work is that these results could, in the future, make it possible to estimate code quality as a function of developers' toxic comments.

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An exploratory study on the influence of developers in technical debt

Cited by 21 publications

References 18 publications

Influence of Developer Factors on Code Quality: A Data Study

Influence of Developer Factors on Code Quality: A Data Study

Anticipatory development processes for reducing total ownership costs and schedules

Analyzing the Correlation Between Toxic Comments and Code Quality

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