Q-Rapids: Quality-Aware Rapid Software Development: an H2020 Project

This work reports the objectives, current state, and outcomes of the Q-Rapids H2020 project. Q-Rapids (Quality-Aware Rapid Software Development) proposes a data-driven approach to the production of software following very short development cycles. The focus of Q-Rapids is on quality aspects, represented through quality requirements. The Q-Rapids platform, which is the tangible software asset emerging from the project, mines software repositories and usage logs to identify candidate quality requirements that may ameliorate the values of strategic indicators like product quality, time to market or team productivity. Four companies are providing use cases to evaluate the platform and associated processes.Peer ReviewedPostprint (author's final draft

Definition of the on-time delivery indicator in rapid software development

Rapid software development (RSD) is an approach for developing software in rapid iterations. One of the critical success factors of an RSD project is to deliver the product releases on time and with the planned features. In this paper, we elaborate an exploratory definition of the On-Time Delivery strategic indicator in RSD based on the literature and interviews with four companies. This indicator supports decision-makers to detect development problems in order to avoid delays and to estimate the additional time needed when requirements, and specifically quality requirements, are considered.Peer ReviewedPostprint (author's final draft

Software analytics tools: an intentional view

Software analytic tools consume big amounts of data coming from either (or both) the software development process or the system usage and aggregate them into indicators which are rendered to different types of stakeholders, also offering them a portfolio of techniques and capabilities such as what-if analysis, prediction and alerts. Precisely, the variety of stakeholders and the different goals they pursue justifies the convenience of performing an intentional analysis of the use of software analytics tools. With this aim, we first enumerate the different stakeholders and identify their intentional relationships with software analytics tools in the form of dependencies. Then, we focus on one particular stakeholder, namely the requirements engineer, and identify further intentional elements represented in a strategic rationale model. The resulting model provides an abstract view of the domain which may help stakeholders when deciding on the adoption of software analytic tools in their particular context.This paper has been funded by the Spanish Ministerio de Ciencia e Innovación under project / funding scheme PID2020-117191RB-I00 / AEI/10.13039/501100011033.Peer ReviewedPostprint (published version

A Quality Model for Actionable Analytics in Rapid Software Development

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 semi-structured 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.Comment: This is an Author's Accepted Manuscript of a paper to be published by IEEE in the 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2018. The final authenticated version will be available onlin

Using Bayesian networks to estimate strategic indicators in the context of rapid software development

Background: During Rapid Software Development, a large amount of project and development data can be collected from different and heterogeneous data sources. Aims: Design a methodology to process these data and turn it into relevant strategic indicators to help companies make meaningful decisions. Method: We adapt an existing methodology to create and estimate strategic indicators using Bayesian Networks in the context of Rapid Software Development, and applied it to a use case. Results: Applying the methodology in the use case, we create a model to predict product quality based on software factors and metrics, using companies’ business knowledge and collected data. Conclusions: We proved the methodology’s feasibility and obtained positive feedback from the company’s use case.Postprint (author's final draft

Data-driven requirements engineering in agile projects: The Q-Rapids approach

Requirements identification, specification and management are key activities in the software development process. In the last years, many approaches to these activities have emerged, based on the exploitation of huge amounts of data gathered from software repositories and system usage. The Q-Rapids project proposes the collection and analysis of such data and its consolidation into a set of strategic indicators as product quality, time to market and team productivity. These indicators are visualized through a dashboard designed to support decision-makers. In this paper, we present the ongoing research undertaken in this project. We use the concept of blocking situation to exemplify the Q-Rapids approach.Peer ReviewedPostprint (author's final draft

Towards an ontology for strategic decision making: The case of quality in rapid software development projects

Strategic decision making is the process of selecting a logical and informed choice from the alternative options based on key strategic indicators determining the success of a specific organization strategy. To support this process and provide a common underlying language, in this work, we present an empirically-grounded ontology to support different strategic decision-making processes and extend the ontology to cover the context of managing quality in Rapid Software Development projects. We illustrate the complete ontology with an example.Peer ReviewedPostprint (author's final draft

Conflicts and synergies among quality requirements

Analyses of the interactions among quality requirements (QRs) have often found that optimizing on one QR will cause serious problems with other QRs. As just one relevant example, one large project had an Integrated Product Team optimize the system for Security. In doing so, it reduced its vulnerability profile by having a single-agent key distribution system and a single copy of the data base – only to have the Reliability engineers point on that these were system-critical single points of failure. The project’s Security-optimized architecture also created conflicts with the system’s Performance, Usability, and Modifiability. Of course, optimizing the system for Security had synergies with Reliability in having high levels of Confidentiality, Integrity, and Availability. This panel aims at fostering discussion on these relationships among QRs and how the use of data repositories may help discovering them.Peer ReviewedPostprint (author's final draft

Data-driven elicitation, assessment and documentation of quality requirements in agile software development

Quality Requirements (QRs) are difficult to manage in agile software development. Given the pressure to deploy fast, quality concerns are often sacrificed for the sake of richer functionality. Besides, artefacts as user stories are not particularly well-suited for representing QRs. In this exploratory paper, we envisage a data-driven method, called Q-Rapids, to QR elicitation, assessment and documentation in agile software development. Q-Rapids proposes: 1) The collection and analysis of design and runtime data in order to raise quality alerts; 2) The suggestion of candidate QRs to address these alerts; 3) A strategic analysis of the impact of such requirements by visualizing their effect on a set of indicators rendered in a dashboard; 4) The documentation of the requirements (if finally accepted) in the backlog. The approach is illustrated with scenarios evaluated through a questionnaire by experts from a telecom company.Peer ReviewedPostprint (author's final draft