Data-Driven Requirements Engineering in Agile Projects: The Q-Rapids Approach

Franch, Xavier; Ayala, Claudia; López, Lidia; Martínez‐Fernández, Silverio; Rodríguez, Pilar; Gómez, Cristina; Jedlitschka, Andreas; Oivo, Markku; Partanen, Jari; Raty, Timo; Rytivaara, Veikko

doi:10.1109/rew.2017.85

Cited by 24 publications

(26 citation statements)

References 11 publications

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“…The idea of feedback has also been discussed in [1], claimed to be one of the critical success factors, promoting a culture of learning. The Q-Rapids solution addresses most of these success factors through its value additions [9], [10], [12]- [14], discussed later in Section V. Our attempt is to determine if these factors indeed play a role in operationalizing a metrics program, or if different factors have emerged owing to the modern-day software development methods.…”

Section: Mendonça and Basilimentioning

confidence: 99%

“…The goal of the EU Horizon 2020 Q-Rapids 2 project is to develop an agile-based, data-driven, quality-aware rapid software development framework [9]. The framework includes tools and methods to elicit and manage quality requirements.…”

Section: A Research Settingmentioning

confidence: 99%

“…We conducted our research in the context of the H2020 Q-Rapids 1 project, which aims at developing an agile-based, data-driven, quality-aware rapid software development framework [9]. This grants us a suitable opportunity to study the factors needed for a successful metrics program in ASD.…”

Section: Introductionmentioning

confidence: 99%

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Success Factors for Effective Process Metrics Operationalization in Agile Software Development: A Multiple Case Study

Ram

Rodríguez

Oivo

et al. 2019

2019 IEEE/ACM International Conference on Software and System Processes (ICSSP)

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Existing literature proposes success factors for establishing metrics programs. However, very few studies focus on factors that could ensure long-term use of metrics, and even fewer studies investigate such factors in the context of Agile Software Development (ASD). Motivated by this knowledge gap, we aim to identify success factors for operationalizing metrics in ASD, particularly, factors that could help in the long-term use of metrics. We conducted a multiple case study, where we operationalized process metrics at two software-intensive companies using ASD. We learned that data availability and development process are the two fundamental success factors for process metrics operationalization, albeit less prominent in literature. Companies prefer iterative and incremental operationalization of stable and functional process metrics, which is analogous to the agile way of working. Metrics trustworthiness plays a key role in successful operationalization of process metrics, and is potentially vital to ensuring their longterm use. By comparing the identified success factors with the existing literature, we conclude that success factors concerning data availability, development process, and metrics trustworthiness warrant greater attention, especially to maximize the chances of long-term use of process metrics.

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Section: Mendonça and Basilimentioning

confidence: 99%

Section: A Research Settingmentioning

confidence: 99%

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Success Factors for Effective Process Metrics Operationalization in Agile Software Development: A Multiple Case Study

Ram

Rodríguez

Oivo

et al. 2019

2019 IEEE/ACM International Conference on Software and System Processes (ICSSP)

Self Cite

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“…Table 2 shows the details of these workshops: Instead of starting from scratch, we built on top of a preliminary set of process metrics that were already identified by the case companies during earlier project tasks [9]. One of the first tasks in the project was to develop a 'Quality Model' that helps the case companies better define their understanding of quality [8,9]. Software quality workshops were held to define this Quality Model, consisting of application of GQM+Strategies™, Quamoco, and GQM.…”

Section: Data Collectionmentioning

confidence: 99%

Software Process Measurement and Related Challenges in Agile Software Development: A Multiple Case Study

Ram

Rodríguez

Oivo

2018

Product-Focused Software Process Improvement

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Existing scientific literature highlights the importance of metrics in Agile Software Development (ASD). Still, empirical investigation into metrics in ASD is scarce, particularly in identifying the rationale and the operational challenges associated with metrics. Under the Q-Rapids project (Horizon 2020), we conducted a multiple case study at four Agile companies. We used the Goal Question Metric (GQM) approach to investigate the rationale explaining the choice of process metrics in ASD, and challenges faced in operationalizing them. Results reflect that companies are interested in assessing process aspects like velocity, testing performance, and estimation accuracy, and they prefer custom metrics for these assessments. Companies use metrics as a means to access and even capitalize on the data, erstwhile inaccessible due to technical or process constraints. However, development context of a company can hinder metrics operationalization, manifesting primarily as unavailability of the data required to measure metrics. The other challenge is the uncertain potential of metrics to help derive actionable inputs to facilitate decision-making. Essentially, development context has a strong influence over a company's choice of process metrics, rationale, and challenges to operationalize these metrics.

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“…In the H2020 Q-Rapids project (Q-Rapids 2017; Franch et al 2017;Guzmán et al 2017), the concept of qualityaware decision making based on key strategic indicators is proposed. The overall goal of the project is to support strategic decision-making processes by providing strategic indicators in the context of quality requirements in agile and rapid software development.…”

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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Data-Driven Requirements Engineering in Agile Projects: The Q-Rapids Approach

Cited by 24 publications

References 11 publications

Success Factors for Effective Process Metrics Operationalization in Agile Software Development: A Multiple Case Study

Success Factors for Effective Process Metrics Operationalization in Agile Software Development: A Multiple Case Study

Software Process Measurement and Related Challenges in Agile Software Development: A Multiple Case Study

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

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