QuASE: A Tool Supported Approach to Facilitating Quality-Related Communication in Software Development

Shekhovtsov, Vladimir A.; Mayr, Heinrich C.; Lubenskyi, Vladyslav

doi:10.1109/quatic.2014.28

Cited by 3 publications

(2 citation statements)

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“…Examples of commercial tools include Microsoft Azure Application Insights, Codacy, Seerene, and Revulytics. Regarding academic tools, examples are SQUALE [6], QuASE [7], CodeFeedr [8], and the Q-Rapids tool [9] (novel in generating data-driven quality requirements from both runtime and design-time data [10]). However, as argued by Dam et al: "One of the key reasons is that software practitioners are reluctant to trust predictions produced by the analytics machinery without understanding the rationale for those predictions" [11].…”

Section: Introductionmentioning

confidence: 99%

A Quality Model for Actionable Analytics in Rapid Software Development

Martínez‐Fernández

Jedlitschka

Guzmán

et al. 2018

2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

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Background: Accessing relevant data on the product, process, and usage perspectives of software as well as integrating and analyzing such data is crucial for getting reliable and timely actionable insights aimed at continuously managing software quality in Rapid Software Development (RSD). In this context, several software analytics tools have been developed in recent years. However, there is a lack of explainable software analytics that software practitioners trust. Aims: We aimed at creating a quality model (called Q-Rapids quality model) for actionable analytics in RSD, implementing it, and evaluating its understandability and relevance. Method: We performed workshops at four companies in order to determine relevant metrics as well as product and process factors. We also elicited how these metrics and factors are used and interpreted by practitioners when making decisions in RSD. We specified the Q-Rapids quality model by comparing and integrating the results of the four workshops. Then we implemented the Q-Rapids tool to support the usage of the Q-Rapids quality model as well as the gathering, integration, and analysis of the required data. Afterwards we installed the Q-Rapids tool in the four companies and performed semistructured interviews with eight product owners to evaluate the understandability and relevance of the Q-Rapids quality model. Results: The participants of the evaluation perceived the metrics as well as the product and process factors of the Q-Rapids quality model as understandable. Also, they considered the Q-Rapids quality model relevant for identifying product and process deficiencies (e.g., blocking code situations). Conclusions: By means of heterogeneous data sources, the Q-Rapids quality model enables detecting problems that take more time to find manually and adds transparency among the perspectives of system, process, and usage.

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Section: Introductionmentioning

confidence: 99%

A Quality Model for Actionable Analytics in Rapid Software Development

Martínez‐Fernández

Jedlitschka

Guzmán

et al. 2018

2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

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“…The QuASE project 1 [22][23][24][25][26][27][28][29] aims at a comprehensive solution for these issues (Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

Facilitating Effective Stakeholder Communication in Software Development Processes

Shekhovtsov

Mayr

Kop

2015

Lecture Notes in Business Information Processing

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Abstract. Effective communication in software development is impaired when parties perceive communicated information differently. To address this problem, the project QuASE has been established. It aims at a solution that supports understandability and reusability of communicated information as well as the quality of decisions based on such information. In this paper, we focus on the architectural aspects of the QuASE system and on its knowledge base which consists of two ontologies: a site ontology defining the site-specific communication environment, and a "quality ontology" that incorporates all knowledge necessary for supporting communication. We describe the overall architecture of the system, introduce the ontologies as well as their interplay, and outline the approach for gathering knowledge necessary to form the QuASE site ontology.

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