Simulation of software maintenance process, with and without a work‐in‐process limit

Concas, Giulio; Lunesu, Maria Ilaria; Marchesi, Michele; Zhang, Hongyu

doi:10.1002/smr.1599

Cited by 17 publications

(14 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To date, research has not sufficiently studied or addressed these characteristics (e.g. Cocco et al, 2011;Concas et al, 2013). Secondly, the effectiveness of Kanban has largely been supported by anecdotal evidence and largely by consultancy organisations whose primary business is based on these purported benefits (i.e.…”

Section: Introductionmentioning

confidence: 99%

Kanban in software engineering: A systematic mapping study

Ahmad

Dennehy

Conboy

et al. 2018

Journal of Systems and Software

101

View full text Add to dashboard Cite

Following a well-established track record of success in other domains such as manufacturing, Kanban is increasingly used to achieve continuous development and delivery of value in the software industry. However, while research on Kanban in software is growing, these articles are largely descriptive, and there is limited rigorous research on its application and with little cohesive building of cumulative knowledge. As a result, it is extremely difficult to determine the true value of Kanban in software engineering. This study investigates the scientific evidence to date regarding Kanban by conducting a systematic mapping of Kanban literature in software engineering between 2006 and 2016. The search strategy resulted in 382 studies, of which 23 were identified as primary papers relevant to this research. This study is unique as it compares the findings of these primary papers with insights from a review of 23 Kanban experience reports during the same period. This study makes four important contributions, (i) a state-of-the-art of Kanban research is provided, (ii) the reported benefits and challenges are identified in both the primary papers and experience reports, (iii) recommended practices from both the primary papers and experience reports are listed and (iv) opportunities for future Kanban research are identified.

show abstract

Section: Introductionmentioning

confidence: 99%

Kanban in software engineering: A systematic mapping study

Ahmad

Dennehy

Conboy

et al. 2018

Journal of Systems and Software

101

View full text Add to dashboard Cite

show abstract

“…However, there are other reports, such as (Concas et al 2013), where there is not much information on the simulation model and its validity, as well as the experimental design, but it also presents a good analysis of the results obtained. Indeed, for both reports, there is no explicit mention to the whole methodology adopted.…”

Section: Analysis Against the Technical Literaturementioning

confidence: 93%

“…Authors usually refer to them as limitations and unverified assumptions, without discussing their consequences. Al-Emran et al (2010) and Concas et al (2013) present discussions according to the types of threats proposed in (Cook and Campbell 1979).…”

Section: Analysis Against the Technical Literaturementioning

confidence: 99%

“…Additionally, we searched for simulation studies in the main conference (ICSSP 3 ) and journal (SPIP, 4 including its new title JSEP), applying the same criteria from the qSLR. We found seven other simulation studies(Al-Emran et al 2010;Birkhölzer et al 2010;Houston and Lieu 2010;Bai et al 2012;Paikari et al 2012;Concas et al 2013;Houston and Buettner 2013) in these venues.…”

mentioning

confidence: 94%

See 1 more Smart Citation

Experimentation with dynamic simulation models in software engineering: planning and reporting guidelines

França

Travassos

2015

Empir Software Eng

View full text Add to dashboard Cite

Simulation-based studies (SBS) have become an interesting investigation approach for Software Engineering (SE). However, the reports on experiments with dynamic simulation models found in the technical literature lack relevant information, hampering the full understanding of the procedures and results reported, as well as their replicability. Apart from the limitations on the length in conferences and journal papers, some of the relevant information seems to be missing due to methodological issues not considered when conducting such studies. This is the case of missing research questions and goals, lack of evidence regarding the dynamic simulation model validity, poorly designed simulation experiments, amongst others. Based on findings from a previous quasi-systematic literature review, we propose a set of reporting guidelines for SBS with dynamic models in the context of SE aiming at providing guidance on which information the report should contain. Furthermore, these guidelines were evolved to support SBS planning by identifying potential threats to simulation study validity and in making recommendations to avoid them, through qualitative analysis and external evaluation. Finally, we conducted different evaluations regarding both the reporting and planning guidelines, apart from using them to support the planning of a SBS as regards software evolution. A set of 33 reporting and planning guidelines for different stages of the simulation lifecycle and focused on the experimentation with dynamic simulation models have been put together. The first assessments point to a comprehensive set of guidelines, supporting a comprehensive preparation and review of the plans and reports from the studies, apart from the planning of a SBS focused on software evolution, potentially reducing the threats to the experimentation with the validity of dynamic simulation models. The 33 guidelines cannot be understood as separate groups for reporting and planning as they overlap in many aspects. The Empir Software Eng main goal is to use the guidelines to support the planning of a simulation-based study with dynamic models so that experimenters may identify potential threats to validity and produce relevant information for a complete simulation experiment report in advance. Despite their initial contribution to increase the validity of SBS, the reporting and planning of simulationbased experiments with dynamic models still has to be discussed and improved in SE. Therefore, additional assessments of this set of guidelines are needed to strengthen the confidence in their completeness and usefulness.

show abstract

“…The work presented in this article emerges from the various di culties regarding the use of simulation models to reproduce real case studies from China and India (Concas et al, 2013;Anderson et al, 2012Anderson et al, , 2011Lunesu, 2013). This article thus contributes to the body of knowledge by presenting a simulationbased approach that can help reflecting on past projects and selecting and evaluating process alternatives to improve the GSE development approach.…”

Section: Related Workmentioning

confidence: 99%

Using simulation for understanding and reproducing distributed software development processes in the cloud

Lunesu

Münch

Marchesi

et al. 2018

Information and Software Technology

Self Cite

View full text Add to dashboard Cite

Context: Organizations increasingly develop software in a distributed manner. The Cloud provides an environment to create and maintain software-based products and services. Currently, it is unknown which software processes are suited for Cloud-based development and what their e↵ects in specific contexts are. Objective: We aim at better understanding the software process applied to distributed software development using the Cloud as development environment. We further aim at providing an instrument, which helps project managers comparing di↵erent solution approaches and to adapt team processes to improve future project activities and outcomes. Method: We provide a simulation model, which helps analyzing di↵erent project parameters and their impact on projects performed in the Cloud. To evaluate the simulation model, we conduct di↵erent analyses using a Scrumban process and data from a project executed in Finland and Spain. An extra adaptation of the simulation model for Scrum and Kanban was used to evaluate the suitability of the simulation model to cover further process models. Results: A comparison of the real project data with the results obtained from the di↵erent simulation runs shows the simulation producing results close to the real data, and we could successfully replicate a distributed software project. Furthermore, we could show that the simulation model is suitable to address further process models. Conclusion: The simulator helps reproducing activities, developers, and events in the project, and it helps analyzing potential tradeo↵s, e.g., regarding throughput, total time, project size, team size and work-inprogress limits. Furthermore, the simulation model supports project managers selecting the most suitable planning alternative thus supporting decision-making processes.

show abstract

Simulation of software maintenance process, with and without a work‐in‐process limit

Cited by 17 publications

References 25 publications

Kanban in software engineering: A systematic mapping study

Kanban in software engineering: A systematic mapping study

Experimentation with dynamic simulation models in software engineering: planning and reporting guidelines

Using simulation for understanding and reproducing distributed software development processes in the cloud

Contact Info

Product

Resources

About