Abstract
To sustain growth, maintain competitive advantage and to innovate, companies must make a paradigm shift in which both short- and long-term value aspects are employed to guide their decision-making. Such need is clearly pressing in innovative industries, such as ICT, and is also the core of Value-based Software Engineering (VBSE). The goal of this paper is to detail a framework called VALUE—improving decision-making relating to software-intensive products and services development—and to show its application in practice to a large ICT company in Finland. The VALUE framework includes a mixed-methods approach, as follows: to elicit key stakeholders’ tacit knowledge regarding factors used during a decision-making process, either transcripts from interviews with key stakeholders are analysed and validated in focus group meetings or focus-group meeting(s) are directly applied. These value factors are later used as input to a Web-based tool (Value tool) employed to support decision making. This tool was co-created with four industrial partners in this research via a design science approach that includes several case studies and focus-group meetings. Later, data on key stakeholders’ decisions gathered using the Value tool, plus additional input from key stakeholders, are used, in combination with the Expert-based Knowledge Engineering of Bayesian Network (EKEBN) process, coupled with the weighed sum algorithm (WSA) method, to build and validate a company-specific value estimation model. The application of our proposed framework to a real case, as part of an ongoing collaboration with a large software company (company A), is presented herein. Further, we also provide a detailed example, partially using real data on decisions, of a value estimation Bayesian network (BN) model for company A. This paper presents some empirical results from applying the VALUE Framework to a large ICT company; those relate to eliciting key stakeholders’ tacit knowledge, which is later used as input to a pilot study where these stakeholders employ the Value tool to select features for one of their company’s chief products. The data on decisions obtained from this pilot study is later applied to a detailed example on building a value estimation BN model for company A. We detail a framework—VALUE framework—to be used to help companies improve their value-based decisions and to go a step further and also estimate the overall value of each decision.
Highlights
Introduction and motivationMany ICT companies worldwide use only cost and effort estimates when making decisions relating to their software/software-intensive products
This paper presents some empirical results from applying the VALUE Framework to a large ICT company; those relate to eliciting key stakeholders’ tacit knowledge, which is later used as input to a pilot study where these stakeholders employ the Value tool to select features for one of their company’s chief products
Implications of this work to research This is the first time that a framework that incorporates and intertwines a conceptual framework of knowledge creation (Nonaka and Toyama 2003), Web-based tool support, and a process for the knowledge engineering of Bayesian networks (Mendes 2012), is put forward for use within the context of value-based software engineering, as means to help improve value-based decision-making, and to estimate the value of decisions relating to software/software-intensive products
Summary
Many ICT companies worldwide use only cost and effort estimates when making decisions relating to their software/software-intensive products. The contribution of this paper is threefold: (i) to detail our proposed VALUE framework, aimed to support our industry partners, and other companies who wish to improve their valuebased decision-making; (ii) to present and discuss some empirical results relating to two of the five parts of that framework, i.e. the elicitation of value factors and their use with tool support for decision-making and (iii) to describe, via a research validation, the last three parts of the framework, where an example, partially based on real data on decisions gathered during the pilot study, explains step-by-step an approach to building and validating a value estimation model using BNs. Note that there are situations when a new technique/method/solution is invented and described without prior adoption in an industrial setting because its uptake can take some time from months to even years (Wieringa and Heerkens 2006).
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