Towards improving decision making and estimating the value of decisions in value-based software engineering: the VALUE framework

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 softwareintensive 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 decisionmaking 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

Using Bayesian Networks to estimate Strategic Indicators in the context of Rapid Software Development

“…In this section, we introduce the methodology used to build a SI BN prediction model in the context of RSD. It is based on the EKEBN (Expert-based Knowledge Engineering of Bayesian Networks) methodology [14] and the Weighted Sum Algorithm (WSA) [8], adapted to the Q-Rapids context. BNs allow incorporating company business knowledge and representing uncertainty that is inherent in a complex domain.…”

“…BNs allow incorporating company business knowledge and representing uncertainty that is inherent in a complex domain. Such representation and quantification use probabilities, and model the cause and effect relationships between the variables of a BN model [8]. BN models can be created using expert knowledge, pure data-driven approaches or a combination of both.…”

“…The WSA uses expert knowledge to identify the probabilities of the most compatible states for a child node's parent nodes, and to assign weights to the parent nodes, representing their importance when computing the child's probabilities. A detailed explanation of the WSA algorithm is given in [8]. Such solution reduces the number of probabilities to be elicited.…”

“…As far as we know, no previous study has proposed a general methodology for defining SIs prediction models within the context of RSD [2]. However, there exist several studies proposing individual SIs using BNs in the area of software engineering, specifically, to estimate "teamwork quality" [3], to model quality in software projects [4], to estimate effort in software development [5], for requirements engineering [6], to predict software defects [7], and to estimate value and help decision making [8,9]. The difference between these works and ours is that we build a SI BN prediction model based upon companies' business knowledge and data automatically measured from heterogeneous data sources, and within the context of RSD.…”

Data-Driven Elicitation of Quality Requirements in Agile Companies

“…To assist the decision maker on instantiating the parameter(s) with appropriate values to solve the quality alert, Q-Rapids will provide simulation techniques that will show the impact that the instantiated QR will have on the elements of the quality model. Such simulation techniques will be based on bayesian networks as proposed in the VALUE framework [25].…”