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Title [en]
SERT- Software Engineering ReThought
Abstract [en]
SERT – Software Engineering ReThought is a groundbreaking research project with the aim to take on the next generation challenges facing companies developing software intensive systems and products. We as an engineering lab are blazing the road introducing 3:rd generation empirical software engineering – denoting close co-production of pragmatic problem solving in close collaboration with our industrial partners as we perform engineering research into topics critical for engineering and business success. SERTs formulation of 3:rd generation empirical software engineering will utilize related knowledge areas as catalysts to solve challenges. Value-based engineering, Data-driven evidence based engineering, and Human-based development will complement software engineering competence in an integrated eco-system of competence focused on the challenges at hand.All areas in software engineering, ranging from inception, realization to evolution are part of the research venture – reflecting that companies need solutions covering their entire ecosystem.
Publications (10 of 142) Show all publications
Paudel, B., Gonzalez-Huerta, J., Mendez, D. & Klotins, E. (2025). A Data-Driven Approach to Optimize Internal Software Quality and Customer Value Delivery. In: Pfahl D., Anwar H., Gonzalez Huerta J., Klünder J. (Ed.), Product-Focused Software Process Improvement. Industry-, Workshop-, and Doctoral Symposium Papers: . Paper presented at 25th International Conference on Product-Focused Software Process Improvement, PROFES 2024, Tartu, Dec 2-4, 2024 (pp. 179-185). Springer Science+Business Media B.V., 15453
Open this publication in new window or tab >>A Data-Driven Approach to Optimize Internal Software Quality and Customer Value Delivery
2025 (English)In: Product-Focused Software Process Improvement. Industry-, Workshop-, and Doctoral Symposium Papers / [ed] Pfahl D., Anwar H., Gonzalez Huerta J., Klünder J., Springer Science+Business Media B.V., 2025, Vol. 15453, p. 179-185Conference paper, Published paper (Refereed)
Abstract [en]

The growing complexity, the ever-ending demands for new features, and the need to become faster to remain competitive force software development organizations to rethink their development and value delivery practices. While continuous delivery has become more popular, it still relies mainly on internal metrics, ad-hoc data, and expert opinions. As a result, software organizations stumble to find the balance between improving internal system quality and delivering external value. In fact, understanding and measuring customer value is on itself essential. In this PhD project, we aim for a better understanding of customer value and develop measurement instruments to be integrated with internal perspectives to drive proactive and continuous internal improvement while delivering relevant customer value. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2025
Series
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743, E-ISSN 1611-3349 ; 15453
Keywords
Continuous Customer Value Delivery, Data-Driven Approach, Software Quality Improvement, Sales, Competitive forces, Customer values, Expert opinion, Quality value, Software development organizations, Software Quality, Software quality improvements, Value delivery
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-27310 (URN)10.1007/978-3-031-78392-0_13 (DOI)2-s2.0-85211242536 (Scopus ID)9783031783913 (ISBN)
Conference
25th International Conference on Product-Focused Software Process Improvement, PROFES 2024, Tartu, Dec 2-4, 2024
Funder
Knowledge Foundation, 20180010
Available from: 2024-12-26 Created: 2024-12-26 Last updated: 2024-12-26Bibliographically approved
Frattini, J., Fucci, D., Torkar, R., Montgomery, L., Unterkalmsteiner, M., Fischbach, J. & Mendez, D. (2025). Applying bayesian data analysis for causal inference about requirements quality: a controlled experiment. Empirical Software Engineering, 30(1), Article ID 29.
Open this publication in new window or tab >>Applying bayesian data analysis for causal inference about requirements quality: a controlled experiment
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2025 (English)In: Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 30, no 1, article id 29Article in journal (Refereed) Published
Abstract [en]

It is commonly accepted that the quality of requirements specifications impacts subsequent software engineering activities. However, we still lack empirical evidence to support organizations in deciding whether their requirements are good enough or impede subsequent activities. We aim to contribute empirical evidence to the effect that requirements quality defects have on a software engineering activity that depends on this requirement. We conduct a controlled experiment in which 25 participants from industry and university generate domain models from four natural language requirements containing different quality defects. We evaluate the resulting models using both frequentist and Bayesian data analysis. Contrary to our expectations, our results show that the use of passive voice only has a minor impact on the resulting domain models. The use of ambiguous pronouns, however, shows a strong effect on various properties of the resulting domain models. Most notably, ambiguous pronouns lead to incorrect associations in domain models. Despite being equally advised against by literature and frequentist methods, the Bayesian data analysis shows that the two investigated quality defects have vastly different impacts on software engineering activities and, hence, deserve different levels of attention. Our employed method can be further utilized by researchers to improve reliable, detailed empirical evidence on requirements quality. © The Author(s) 2024.

Place, publisher, year, edition, pages
Springer, 2025
Keywords
Bayesian data analysis, Experiment, Replication, Requirements engineering, Requirements quality, Data accuracy, Data assimilation, Data consistency, Spatio-temporal data, Causal inferences, Controlled experiment, Domain model, Engineering activities, Quality defects, Requirement engineering, Requirement quality, Requirements specifications, Software quality
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-27175 (URN)10.1007/s10664-024-10582-1 (DOI)2-s2.0-85209711862 (Scopus ID)
Funder
Knowledge Foundation, 20180010
Available from: 2024-11-29 Created: 2024-11-29 Last updated: 2025-01-16Bibliographically approved
Al-Saedi, A. A., Boeva, V. & Casalicchio, E. (2025). Contribution Prediction in Federated Learning via Client Behavior Evaluation. Future Generation Computer Systems, 166, Article ID 107639.
Open this publication in new window or tab >>Contribution Prediction in Federated Learning via Client Behavior Evaluation
2025 (English)In: Future Generation Computer Systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 166, article id 107639Article in journal (Refereed) Published
Abstract [en]

Federated learning (FL), a decentralized machine learning framework that allows edge devices (i.e., clients) to train a global model while preserving data/client privacy, has become increasingly popular recently. In FL, a shared global model is built by aggregating the updated parameters in a distributed manner. To incentivize data owners to participate in FL, it is essential for service providers to fairly evaluate the contribution of each data owner to the shared model during the learning process. To the best of our knowledge, most existing solutions are resource-demanding and usually run as an additional evaluation procedure. The latter produces an expensive computational cost for large data owners. In this paper, we present simple and effective FL solutions that show how the clients’ behavior can be evaluated during the training process with respect to reliability, and this is demonstrated for two existing FL models, Cluster Analysis-based Federated Learning (CA-FL) and Group-Personalized FL (GP-FL), respectively. In the former model, CA-FL, the frequency of each client to be selected as a cluster representative and in that way to be involved in the building of the shared model is assessed. This can eventually be considered as a measure of the respective client data reliability. In the latter model, GP-FL, we calculate how many times each client changes a cluster it belongs to during FL training, which can be interpreted as a measure of the client's unstable behavior, i.e., it can be considered as not very reliable. We validate our FL approaches on three LEAF datasets and benchmark their performance to two baseline contribution evaluation approaches. The experimental results demonstrate that by applying the two FL models we are able to get robust evaluations of clients’ behavior during the training process. These evaluations can be used for further studying, comparing, understanding, and eventually predicting clients’ contributions to the shared global model.

Place, publisher, year, edition, pages
Elsevier, 2025
Keywords
Behavior monitoring; Clustering analysis, Contribution evaluation, Eccentricity analysis, Federated learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-26080 (URN)10.1016/j.future.2024.107639 (DOI)2-s2.0-85211047272 (Scopus ID)
Funder
Knowledge Foundation, 20220068Knowledge Foundation, 20180010
Available from: 2024-04-05 Created: 2024-04-05 Last updated: 2024-12-17Bibliographically approved
Yu, L., Alégroth, E., Chatzipetrou, P. & Gorschek, T. (2025). Experience with Large Language Model Applications for Information Retrieval from Enterprise Proprietary Data. In: Dietmar Pfahl, Javier Gonzalez Huerta, Jil Klünder, Hina Anwar (Ed.), Product-Focused Software Process Improvement: . Paper presented at 25th International Conference on Product-Focused Software Process Improvement, PROFES 2024, Tartu, Dec 2-4, 2024 (pp. 92-107). Springer, 15452
Open this publication in new window or tab >>Experience with Large Language Model Applications for Information Retrieval from Enterprise Proprietary Data
2025 (English)In: Product-Focused Software Process Improvement / [ed] Dietmar Pfahl, Javier Gonzalez Huerta, Jil Klünder, Hina Anwar, Springer, 2025, Vol. 15452, p. 92-107Conference paper, Published paper (Refereed)
Abstract [en]

Large Language Models (LLMs) offer promising capabilities for information retrieval and processing. However, the LLM deployment for querying proprietary enterprise data poses unique challenges, particularly for companies with strict data security policies. This study shares our experience in setting up a secure LLM environment within a FinTech company and utilizing it for enterprise information retrieval while adhering to data privacy protocols. 

We conducted three workshops and 30 interviews with industrial engineers to gather data and requirements. The interviews further enriched the insights collected from the workshops. We report the steps to deploy an LLM solution in an industrial sandboxed environment and lessons learned from the experience. These lessons contain LLM configuration (e.g., chunk_size and top_k settings), local document ingestion, and evaluating LLM outputs.

Our lessons learned serve as a practical guide for practitioners seeking to use private data with LLMs to achieve better usability, improve user experiences, or explore new business opportunities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Place, publisher, year, edition, pages
Springer, 2025
Series
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743, E-ISSN 1611-3349 ; 15452
Keywords
AI, Artificial intelligence, Data security, Information retrieval, Large Language Model, LLM, Sandbox environment, Data privacy, Fintech, Enterprise data, Language model, Model application, Modeling environments, Privacy protocols, Security policy, Structured Query Language
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-27326 (URN)10.1007/978-3-031-78386-9_7 (DOI)2-s2.0-85211960724 (Scopus ID)9783031783852 (ISBN)
Conference
25th International Conference on Product-Focused Software Process Improvement, PROFES 2024, Tartu, Dec 2-4, 2024
Funder
Knowledge Foundation, 20180010
Available from: 2024-12-28 Created: 2024-12-28 Last updated: 2024-12-28Bibliographically approved
Frattini, J. (2025). Good-Enough Requirements Engineering. (Doctoral dissertation). Karlskrona: Blekinge Tekniska Högskola
Open this publication in new window or tab >>Good-Enough Requirements Engineering
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Background: High-quality requirements are considered crucial for successful software development endeavors as the requirements' purpose is to inform subsequent activities like implementation or testing. Requirements quality defects have been shown to incur significant costs for remediation, scaling up even to project failure. At the same time, the effort to improve the quality of requirements must be justified. Organizations developing software, therefore, need to understand when their requirements artifacts are of "good enough'' quality, i.e., they need to be able to identify the optimum between over- and under-engineering.

Problem: The body of knowledge in requirements quality does not yet offer solutions that would allow organizations to identify that optimum due to three shortcomings: (1) there is no generally accepted, theoretical foundation to describe requirements quality that can serve as a basis to coordinate distributed research efforts and the synthesis of evidence in the field, (2) the scientific practice currently applied in the field is of limited rigor to draw reliable conclusions from existing empirical contributions, and (3) the field lacks empirical evidence that can be aggregated to form a holistic view of requirements quality. These are potential causes for the lack of adoption of requirements quality research in practice.

Goal: In this cumulative, publication-based thesis, we address these three shortcomings and aim to contribute to a more evidence-based approach to requirements quality research grounded in scientific theory.

Method: First, we develop a theoretical foundation by adopting and integrating existing software engineering theories. Second, we evaluate the state of the art of data analysis and open science in the field and provide guidelines to improve these practices. Third, we demonstrate the application of these guidelines and conduct a controlled experiment to contribute additional empirical evidence to the field.

Results: The resulting set of analytical theories specifies requirements quality and provides a structure for future empirical contributions. Our evaluation of the state of the art shows both the need for a common theoretical foundation as well as support for applying rigorous research practices. Our empirical studies contribute to these needs and illustrate the complexity of the impact that requirements quality defects have on subsequent activities. Finally, we develop a method for the effective aggregation of empirical results.

Conclusion: Our theoretical, methodological, and empirical contributions help to coordinate a productive and constructive research agenda on requirements quality that is based on evidence and grounded in theory. This allows for rigorous and practically relevant research that ultimately informs organizations on how to engineer good-enough requirements.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2025. p. 257
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 2025:03
Keywords
Requirements Engineering, Requirements Artifacts, Requirements Quality
National Category
Software Engineering
Research subject
Software Engineering
Identifiers
urn:nbn:se:bth-27382 (URN)978-91-7295-496-0 (ISBN)
Public defence
2025-02-28, J1630, Karlskrona, 13:00 (English)
Opponent
Supervisors
Available from: 2025-01-17 Created: 2025-01-16 Last updated: 2025-02-06Bibliographically approved
Sundelin, A., Gonzalez-Huerta, J., Torkar, R. & Wnuk, K. (2025). Governing the commons: code ownership and code-clones in large-scale software development. Empirical Software Engineering, 30(2), Article ID 43.
Open this publication in new window or tab >>Governing the commons: code ownership and code-clones in large-scale software development
2025 (English)In: Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 30, no 2, article id 43Article in journal (Refereed) Published
Abstract [en]

Context: In software development organizations employing weak or collective ownership, different teams are allowed and expected to autonomously perform changes in various components. This creates diversity both in the knowledge of, and in the responsibility for, individual components.

Objective: Our objective is to understand how and why different teams introduce technical debt in the form of code clones as they change different components.

Method: We collected data about change size and clone introductions made by ten teams in eight components which was part of a large industrial software system. We then designed a Multi-Level Generalized Linear Model (MLGLM), to illustrate the teams’ differing behavior. Finally, we discussed the results with three development teams, plus line manager and the architect team, evaluating whether the model inferences aligned with what they expected. Responses were recorded and thematically coded.

Results: The results show that teams do behave differently in different components, and the feedback from the teams indicates that this method of illustrating team behavior can be useful as a complement to traditional summary statistics of ownership.

Conclusions: We find that our model-based approach produces useful visualizations of team introductions of code clones as they change different components. Practitioners stated that the visualizations gave them insights that were useful, and by comparing with an average team, inter-team comparisons can be avoided. Thus, this has the potential to be a useful feedback tool for teams in software development organizations that employ weak or collective ownership. © The Author(s) 2024.

Place, publisher, year, edition, pages
Springer, 2025
Keywords
Bayesian linear model, Code clones, Code ownership, Software craftsmanship, Team behavior, Bayesian, Code clone, Collective ownership, Large-scales, Linear modeling, Software development organizations, Team behaviour
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-27329 (URN)10.1007/s10664-024-10598-7 (DOI)001377050600004 ()2-s2.0-85211925991 (Scopus ID)
Funder
Knowledge Foundation, 20180010
Available from: 2024-12-28 Created: 2024-12-28 Last updated: 2025-01-02Bibliographically approved
Chatzipetrou, P., Šmite, D., Tkalich, A., Moe, N. B. & Klotins, E. (2025). Interest in Working Remotely: Is Gender a Factor?. In: Dietmar Pfahl, Javier Gonzalez Huerta, Jil Klünder, Hina Anwar (Ed.), Product-Focused Software Process Improvement: . Paper presented at 25th International Conference on Product-Focused Software Process Improvement, PROFES 2024, Tartu, Dec 2-4, 2024 (pp. 156-171). Springer, 15452
Open this publication in new window or tab >>Interest in Working Remotely: Is Gender a Factor?
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2025 (English)In: Product-Focused Software Process Improvement / [ed] Dietmar Pfahl, Javier Gonzalez Huerta, Jil Klünder, Hina Anwar, Springer, 2025, Vol. 15452, p. 156-171Conference paper, Published paper (Refereed)
Abstract [en]

Background: Modern workplaces have irreversibly changed their attitudes toward remote working, allowing different degrees of remotely working. Decisions about the influence of restricted remote working and mandatory office presence often raise the question of disproportional impact on different genders.

Aim: Our aim is to achieve a better understanding of whether WFH has a gender-segregated motivation and what other factors predict individual choices to work onsite or remotely.

Method: We report results from a company-wide survey conducted in NorBank, a Norwegian fintech company. The data is analyzed using descriptive statistics, contingency tables, Chi-Square test of association along with post hoc tests. We illustrated the results by using diverged chart bars.

Results: The results show that gender differences among software engineers are negligible and insignificant. Further, software engineers work more remotely than employees in other departments. We also found that engineers without managerial responsibilities are less at the office, and those who live further to their job, tend to work more remotely. With respect to preferences to work remotely, we found that younger engineers choose to work at the office more often than the senior engineers.

Conclusions: We found that the strongest predictor of the degree of remote working is not the gender but commute time and role. This also means that any analysis of general populations (as the analysis of all employees at NorBank) shall be approached with care because it may lead to flawed conclusions due to the different distributions of gender and roles in different departments. 

Place, publisher, year, edition, pages
Springer, 2025
Series
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743, E-ISSN 1611-3349 ; 15452
Keywords
Empirical study, Gender, Hybrid work, Remote work, Software engineering, WHF, Work-from-home, Computer aided software engineering, Human engineering, Human resource management, Population statistics, Software testing, Contingency table, Descriptive statistics, Empirical studies, Individual choice, Remote working, Fintech
National Category
Software Engineering Work Sciences
Identifiers
urn:nbn:se:bth-27328 (URN)10.1007/978-3-031-78386-9_11 (DOI)2-s2.0-85211921052 (Scopus ID)9783031783852 (ISBN)
Conference
25th International Conference on Product-Focused Software Process Improvement, PROFES 2024, Tartu, Dec 2-4, 2024
Funder
Knowledge Foundation, 20220047Knowledge Foundation, 20180010
Available from: 2024-12-30 Created: 2024-12-30 Last updated: 2024-12-30Bibliographically approved
Tran, H. K., Ali, N. b., Unterkalmsteiner, M., Börstler, J. & Chatzipetrou, P. (2025). Quality attributes of test cases and test suites - importance & challenges from practitioners' perspectives. Software quality journal, 33(1), Article ID 9.
Open this publication in new window or tab >>Quality attributes of test cases and test suites - importance & challenges from practitioners' perspectives
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2025 (English)In: Software quality journal, ISSN 0963-9314, E-ISSN 1573-1367, Vol. 33, no 1, article id 9Article in journal (Refereed) Published
Abstract [en]

The quality of the test suites and the constituent test cases significantly impacts confidence in software testing. While research has identified several quality attributes of test cases and test suites, there is a need for a better understanding of their relative importance in practice. We investigate practitioners' perceptions regarding the relative importance of quality attributes of test cases and test suites and the challenges that they face in ensuring the perceived important quality attributes. To capture the practitioners' perceptions, we conducted an industrial survey using a questionnaire based on the quality attributes identified in an extensive literature review. We used a sampling strategy that leverages LinkedIn to draw a large and heterogeneous sample of professionals with experience in software testing. We collected 354 responses from practitioners with a wide range of experience (from less than one year to 42 years of experience). We found that the majority of practitioners rated Fault Detection, Usability, Maintainability, Reliability, and Coverage to be the most important quality attributes. Resource Efficiency, Reusability, and Simplicity received the most divergent opinions, which, according to our analysis, depend on the software-testing contexts. Also, we identified common challenges that apply to the important attributes, namely inadequate definition, lack of useful metrics, lack of an established review process, and lack of external support. The findings point out where practitioners actually need further support with respect to achieving high-quality test cases and test suites under different software testing contexts. Hence, the findings can serve as a guideline for academic researchers when looking for research directions on the topic. Furthermore, the findings can be used to encourage companies to provide more support to practitioners to achieve high-quality test cases and test suites.

Place, publisher, year, edition, pages
Springer, 2025
Keywords
Software testing, Test case quality, Test suite quality, Quality assurance
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-27395 (URN)10.1007/s11219-024-09698-w (DOI)001396622900001 ()
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsKnowledge Foundation, 20220235Knowledge Foundation, 20180010
Available from: 2025-01-24 Created: 2025-01-24 Last updated: 2025-01-24Bibliographically approved
Coppola, R., Feldt, R., Nass, M. & Alégroth, E. (2025). Ranking approaches for similarity-based web element location. Journal of Systems and Software, 222, Article ID 112286.
Open this publication in new window or tab >>Ranking approaches for similarity-based web element location
2025 (English)In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 222, article id 112286Article in journal (Refereed) Published
Abstract [en]

Context: GUI-based tests for web applications are frequently broken by fragility, i.e. regression tests fail due to changing properties of the web elements. The most influential factor for fragility are the locators used in the scripts, i.e. the means of identifying the elements of the GUI.

Objective: We extend a state-of-the-art Multi-Locator solution that considers 14 locators from the DOM model of a web application, and identifies overlapping nodes in the DOM tree (VON-Similo). We augment the approach with standard Machine Learning and Learning to Rank (LTR) approaches to aid the location of web elements.

Method: We document an experiment with a ground truth of 1163 web element pairs, taken from different releases of 40 web applications, to compare the robustness of the algorithms to locator weight change, and the performance of LTR approaches in terms of MeanRank and PctAtN.

Results: Using LTR algorithms, we obtain a maximum probability of finding the correct target at the first position of 88.4% (lowest 82.57%), and among the first three positions of 94.79% (lowest 91.86%). The best mean rank of the correct candidate is 1.57.

Conclusion: The similarity-based approach proved to be highly dependable in the context of web application testing, where a low percentage of matching errors can still be accepted.

Place, publisher, year, edition, pages
Elsevier, 2025
Keywords
GUI testing, Test automation, Test case robustness, Web element locators, XPath locators, Learning to rank, Mean-ranks, Ranking approach, Test case, WEB application, Web applications, Web element locator, Xpath locator, Contrastive Learning
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-27257 (URN)10.1016/j.jss.2024.112286 (DOI)001375573600001 ()2-s2.0-85211062465 (Scopus ID)
Funder
Knowledge Foundation, 20180010
Available from: 2024-12-17 Created: 2024-12-17 Last updated: 2024-12-27Bibliographically approved
Dorner, M., Mendez, D., Wnuk, K., Zabardast, E. & Czerwonka, J. (2025). The upper bound of information diffusion in code review. Empirical Software Engineering, 30(1), Article ID 2.
Open this publication in new window or tab >>The upper bound of information diffusion in code review
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2025 (English)In: Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 30, no 1, article id 2Article in journal (Refereed) Published
Abstract [en]

Background

Code review, the discussion around a code change among humans, forms a communication network that enables its participants to exchange and spread information. Although reported by qualitative studies, our understanding of the capability of code review as a communication network is still limited.

Objective

In this article, we report on a first step towards understanding and evaluating the capability of code review as a communication network by quantifying how fast and how far information can spread through code review: the upper bound of information diffusion in code review.

Method

In an in-silico experiment, we simulate an artificial information diffusion within large (Microsoft), mid-sized (Spotify), and small code review systems (Trivago) modelled as communication networks. We then measure the minimal topological and temporal distances between the participants to quantify how far and how fast information can spread in code review.

Results

An average code review participants in the small and mid-sized code review systems can spread information to between 72 % and 85 % of all code review participants within four weeks independently of network size and tooling; for the large code review systems, we found an absolute boundary of about 11 000 reachable participants. On average (median), information can spread between two participants in code review in less than five hops and less than five days.

Conclusion

We found evidence that the communication network emerging from code review scales well and spreads information fast and broadly, corroborating the findings of prior qualitative work. The study lays the foundation for understanding and improving code review as a communication network.

Place, publisher, year, edition, pages
Springer, 2025
Keywords
Code review, Simulation, Information diffusion, Communication network
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:bth-27028 (URN)10.1007/s10664-024-10442-y (DOI)001335071300002 ()2-s2.0-85206942985 (Scopus ID)
Funder
Knowledge Foundation, 20180010
Available from: 2024-10-30 Created: 2024-10-30 Last updated: 2024-11-04Bibliographically approved
Principal InvestigatorGorschek, Tony
Coordinating organisation
Blekinge Institute of Technology
Funder
Period
2018-09-01 - 2026-09-01
National Category
Software Engineering
Identifiers
DiVA, id: project:2307Project, id: 20180010

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