Q-Rapids framework for advanced data analysis to improve rapid software development

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…The Q-Rapids approach [16] is based on gathering and analysing data from several sources (software repositories, project management tools, system usage and quality of service) [20], [26]. Data is aggregated into quality indicators that are rendered to the different stakeholders by means of the Q-Rapids tool [25].…”

Section: B the Q-rapids Projectmentioning

confidence: 99%

Measuring and Improving Agile Processes in a Small-Size Software Development Company

et al. 2020

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Agile software development has become commonplace in software development companies due to the numerous benefits it provides. However, conducting Agile projects is demanding in Small and Medium Enterprises (SMEs), because projects start and end quickly, but still have to fulfil customers' quality requirements. Objective: This paper aims at reporting a practical experience on the use of metrics related to the software development process as a means supporting SMEs in the development of software following an Agile methodology. Method: We followed Action-Research principles in a Polish small-size software development company. We developed and executed a study protocol suited to the needs of the company, using a pilot case. Results: A catalogue of Agile development process metrics practically validated in the context of a small-size software development company, adopted by the company in their Agile projects. Conclusions: Practitioners may adopt these metrics in their Agile projects, especially if working in an SME, and customise them to their own needs and tools. Academics may use the findings as a baseline for new research work, including new empirical studies.INDEX TERMS Agile software development, process metrics, software engineering, software quality, rapid software development, SMEs.

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Section: B the Q-rapids Projectmentioning

confidence: 99%

Measuring and Improving Agile Processes in a Small-Size Software Development Company

et al. 2020

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“…In our previous Q-Rapids related papers (Kozik et al 2017(Kozik et al , 2018, we showed the presented results of softwarerelated data analysis and correlation.…”

Section: Context and Related Workmentioning

confidence: 99%

Increasing product owners’ cognition and decision-making capabilities by data analysis approach

et al. 2018

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In this paper, we focus on the innovative advanced data analysis dedicated for product owners in software development teams. The goal of our novel solutions is to increase cognition and decision making in rapid software development process. The major contribution of this work is the methodology and the tool that gathers the input raw data from the tools such as GitLab or SonarQube, and processes the data further (e.g., using Apache Kafka, Kibana, and Spark) to calculate and visualize more advanced metrics, product factors (e.g., in accordance to Quamoco model), and indicators, and to find correlations between them. Then, such high-level data are shown to product owners to increase their cognition, situational awareness, and decision-making capabilities. We have now implemented the proof-of-concept system which was positively validated by product owners using the real data from their projects.

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“…While in the previous work we focused on the data gathering and processing it towards quality metrics, indicators and requirements generation [21] [7], we hereby target the prediction of the software related metrics.…”

Section: Introduction and Contextmentioning

confidence: 99%

Software Development Metrics Prediction Using Time Series Methods

Choraś

Kozik

Pawlicki

et al. 2019

Lecture Notes in Computer Science

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The software development process is an intricate task, with the growing complexity of software solutions and inflating code-line count being part of the reason for the fall of software code coherence and readability thus being one of the causes for software faults and it's declining quality. Debugging software during development is significantly less expensive than attempting damage control after the software's release. An automated quality-related analysis of developed code, which includes code analysis and correlation of development data like an ideal solution. In this paper the ability to predict software faults and software quality is scrutinized. Hereby we investigate four models that can be used to analyze time-based data series for prediction of trends observed in the software development process are investigated. Those models are Exponential Smoothing, the Holt-Winters Model, Autoregressive Integrated Moving Average (ARIMA) and Recurrent Neural Networks (RNN). Time-series analysis methods prove a good fit for software related data prediction. Such methods and tools can lend a helping hand for Product Owners in their daily decision-making process as related to e.g. assignment of tasks, time predictions, bugs predictions, time to release etc. Results of the research are presented.

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Q-Rapids framework for advanced data analysis to improve rapid software development

Cited by 6 publications

References 19 publications

Measuring and Improving Agile Processes in a Small-Size Software Development Company

Measuring and Improving Agile Processes in a Small-Size Software Development Company

Increasing product owners’ cognition and decision-making capabilities by data analysis approach

Software Development Metrics Prediction Using Time Series Methods

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