Value-based Software Engineering Research Articles

Value-based Software Engineering: A Systematic Mapping Study

Background: Integrating value-oriented perspectives into the principles and practices of software engineering is fundamental to ensure that software development activities address key stakeholders’ views and also balance short-and long-term goals. This is put forward in the discipline of value-based software engineering (VBSE) Aim: This study aims to provide an overview of VBSE with respect to the research efforts that have been put into VBSE. Method: We conducted a systematic mapping study to classify evidence on value definitions, studies’ quality, VBSE principles and practices, research topics, methods, types, contribution facets, and publication venues. Results: From 143 studies we found that the term “value” has not been clearly defined in many studies. VB Requirements Engineering and VB Planning and Control were the two principles mostly investigated, whereas VB Risk Management and VB People Management were the least researched. Most studies showed very good reporting and relevance quality, acceptable credibility, but poor in rigour. Main research topic was Software Requirements and case study research was the method used the most. The majority of studies contribute towards methods and processes, while very few studies have proposed metrics and tools. Conclusion: We highlighted the research gaps and implications for research and practice to support VBSE.

A Theory of Value for Value-Based Feature Selection in Software Engineering

Value-Based Software Engineering stresses the role of value in software related decisions. In the context of feature selection, software features judged to provide higher value take priority in the development process. This paper focuses on what value means when selecting software features. Using grounded theory, we conducted and analyzed semi-structured interviews with 21 key stakeholders (decision-makers) from three software/software-intensive companies, within a context where value-based decision-making was already established. Our analysis led to the building of a theory of value for value-based feature selection that identifies the nature of value propositions considered by key stakeholders when selecting software features (i.e., decision-making criteria for deciding upon software features, as suggested by Boehm (2003)). We found that some value propositions were common to all three company cases (core value propositions), whereas others were dependent upon the context in which a company operates, and the characteristics of the product under development (specific value propositions). Moreover, value propositions vary according to the stakeholder group and the type of feature being assessed. Our study provides significant insight into value in the context of feature selection, and generates new concepts around value-based feature selection such as new value propositions.

Value-Based Software Engineering: A Systematic Mapping Study

Value-Based Software Engineering: A Systematic Mapping Study

Value‐oriented quality metrics in software development: Practical relevance from a software engineering perspective

When following the principles of value-based software engineering, business, customer satisfaction, and engineering considerations need to be balanced to develop and operate the software so that it satisfies the different stakeholders' expectations. This, however, requires knowing the relevant quality metrics covering these value-oriented expectations and potential sources for their measurement. In this work, a categorisation of value-oriented quality metrics that are practically relevant is presented. Therefore, the authors conducted an online survey with practitioners who assessed the relevance of 61 value-oriented metrics, gathered from a preceding systematic mapping study. The authors grouped these metrics into 10 categories, based on financial, customer satisfaction, value proposition, and creation perspectives. Also, the authors examined the frequency of particular steps at which these measures accrue and identified their most relevant data sources. The participants rated metrics for feature reliability, performance, as well as test and development efficiency as most relevant for value orientation. According to the participants, the authors' collection covers all relevant metrics for addressing financial and market and feature usability aspects. The authors' categorisation and the metrics' relevance assessments shall support software engineers in selecting relevant metrics and their sources for software product development.

Using Bayesian Network to Estimate the Value of Decisions within the Context of Value-Based Software Engineering: A Multiple Case Study

Context: 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 pressing in innovative industries, such as ICT, and is the core of Value-based Software Engineering (VBSE). Objective: This paper details three case studies where value estimation models using Bayesian Network (BN) were built and validated. These estimation models were based upon value-based decisions made by key stakeholders in the contexts of feature selection, test cases execution prioritization, and user interfaces design selection. Methods: All three case studies were carried out according to a Framework called VALUE — improVing decision-mAking reLating to software-intensive prodUcts and sErvices development. This framework includes a mixed-methods approach, comprising several steps to build and validate company-specific value estimation models. Such a building process uses as input data key stakeholders’ decisions (gathered using the Value tool), plus additional input from key stakeholders. Results: Three value estimation BN models were built and validated, and the feedback received from the participating stakeholders was very positive. Conclusions: We detail the building and validation of three value estimation BN models, using a combination of data from past decision-making meetings and also input from key stakeholders.

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

The original version of this article unfortunately contained a mistake in Figs. 1 and 21. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

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

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.

A Review of Value Based Software Engineering and its Impacts

Profitability is the main goal of every software organization. Software companies develop products to capture greater market place and gain the attention of targeted customers or stakeholder. In the past the software engineering field was not able to determine the stakeholder’s value of the developing product as mostly systems were based on value neutral setting. With the emergence of Value based software engineering (VBSE) the traditional software engineering discipline is vastly changed as during product development the stakeholder’s value proposition plays an important part and the development is done accordingly. In this article I cover some important aspects of value based software engineering regarding their impact in software reusability and quality. Different value based pricing criteria are discussed. Empirical research and value based selection of best suitable automated tools and existing Component off the shelf (COTS) and conflicts between different stakeholders of the same system are also taken into account. In the 3rd section Critical review of the all papers is performed and their plus and negative points are mentioned. At last the Conclusion and future work in value based software engineering field is presented.

Software quality across borders: Three case studies on company internal alignment

ContextSoftware quality issues are commonly reported when offshoring software development. Value-based software engineering addresses this by ensuring key stakeholders have a common understanding of quality. ObjectiveThis work seeks to understand the levels of alignment between key stakeholder groups within a company on the priority given to aspects of software quality developed as part of an offshoring relationship. Furthermore, the study aims to identify factors impacting the levels of alignment identified. MethodThree case studies were conducted, with representatives of key stakeholder groups ranking aspects of software quality in a hierarchical cumulative exercise. The results are analysed using Spearman rank correlation coefficients and inertia. The results were discussed with the groups to gain a deeper understanding of the issues impacting alignment. ResultsVarious levels of alignment were found between the various groups. The reasons for misalignment were found to include cultural factors, control of quality in the development process, short-term versus long-term orientations, understanding of cost-benefits of quality improvements, communication and coordination. ConclusionsThe factors that negatively affect alignment can vary greatly between different cases. The work emphasises the need for greater support to align company internal success-critical stakeholder groups in their understanding of quality when offshoring software development.

Improving software testing process: feature prioritization to make winners of success‐critical stakeholders

SUMMARYFor a successful software project, acceptable quality must be achieved within an acceptable cost, demonstrating business value to customers and satisfactorily meeting delivery timeliness. Testing serves as the most widely used approaches to determine that the intended functionalities are performed correctly and achieve the desired level of services; however, it is also a labor‐intensive and expensive process during the whole software life cycle. Most current testing processes are often technique‐centered, rather than organized to maximize business value. In this article, we extend and elaborate the ‘4+1’ theoretical lenses of Value‐based Software Engineering (VBSE) framework in the software testing process; propose a multi‐objective feature prioritization strategy for testing planning and controlling, which aligns the internal testing process with value objectives coming from customers and markets. Our case study in a real‐life business project shows that this method allows reasoning about the software testing process in different dimensions: it helps to manage the testing process effectively and efficiently, provides information for continuous internal software process improvement, and increases customer satisfaction, which makes winners of all success‐critical stakeholders (SCSs) in the software testing process. Copyright © 2010 John Wiley & Sons, Ltd.

A Value-Based Framework for Software Evolutionary Testing

The fundamental objective in value-based software engineering is to integrate consistent stakeholder value propositions into the full extent of software engineering principles and practices so as to increase the value for software assets. In such a value-based setting, artifacts in software development such as requirement specifications, use cases, test cases, or defects, are not treated as equally important during the development process. Instead, they will be differentiated according to how much they are contributing, directly or indirectly, to the stakeholder value propositions. The higher the contributions, the more important the artifacts become. In turn, development activities involving more important artifacts should be given higher priorities and greater considerations in the development process. In this paper, a value-based framework is proposed for carrying out software evolutionary testing with a focus on test data generation through genetic algorithms. The proposed framework incorporates general principles in value-based software testing and makes it possible to prioritize testing decisions that are rooted in the stakeholder value propositions. It allows for a cost-effective way to fulfill most valuable testing objectives first and a graceful degradation when planned testing process has to be shortened.

A framework for eliciting value proposition from stakeholders

Eliciting the value proposition in Value Based Software Engineering (VBSE) is critical. Everything within VBSE is dependent on the value propositions of success critical stakeholder (SCSs). This pa...

Value-Driven Software Maintenance

This paper extends the concept of value-based software engineering as proposed by Boehm to the field of software maintenance. The various approaches to assessing the value of software products and to calculating the return on investment (ROI) on software projects are reviewed. The authors propose a methodology of value-driven software maintenance for assessing the value of software maintenance based on Hayek’s theory of maintaining the value of capital goods in an evolving capital structure. A method for predicting costs of maintenance tasks based on impact analysis is combined with a method for assessing the added value produced by the maintenance tasks to compute the ROI on software projects. The validity of the approach is illustrated through a case study of the German social services payment system.

A Follow-Up Reflection on Software Process Improvement ROI

Our discipline must shift toward value-based software engineering, because we're obliged to prove our contributions to the financial bottom line. In the May/June 2004 IEEE Software special issue on return on investment (ROI), the author presented measurement results for the ROI of software process improvement (SPI). This article made three main contributions. First, provided a detailed overview of publications containing real-life measurement results from practical applications of SPI, in which the author measured the ROI. My study included 20 cases, with an average ROI of 7 and a median of 6.6. This indicates that SPI's net profit seems to be approximately US$7 for every dollar invested. However, I found no published cases in which SPI investments resulted in a measurable loss; furthermore, the ROI bandwidth was large (between 1.5 and 19). This indicates that the actual ROI of an SPI investment seems hard to really guarantee up front. Second, I showed that benefits are just as easy to quantify as costs. Cost measurements are always based on an agreement about how to measure and quantify costs. Such an agreement can also serve as the basis for measuring benefits. My article contained data from two real-life projects that had made such cost and benefit measurements and calculated ROI. Finally, I concluded that expressing value is crucial. Software engineering and its improvement are often major investments for organizations. Investments must be profitable. Because different people in different roles share one generic term for value-money, I recommended expressing any software engineering effort and its benefits in financial terms.

Machine Learning and Value-Based Software Engineering

Software engineering research and practice thus far are primarily conducted in a value-neutral setting where each artifact in software development such as requirement, use case, test case, and defect, is treated as equally important during a software system development process. There are a number of shortcomings of such value-neutral software engineering. Value-based software engineering is to integrate value considerations into the full range of existing and emerging software engineering principles and practices. Machine learning has been playing an increasingly important role in helping develop and maintain large and complex software systems. However, machine learning applications to software engineering have been largely confined to the value-neutral software engineering setting. In this paper, the general message to be conveyed is to apply machine learning methods and algorithms to value-based software engineering. The training data or the background knowledge or domain theory or heuristics or bias used by machine learning methods in generating target models or functions should be aligned with stakeholders’ value propositions. An initial research agenda is proposed for machine learning in value-based software engineering.

Developing a process framework using principles of value-based software engineering

In this article we present a software process framework using the 4 + 1 theory and principles of value-based software engineering (VBSE). The value-based process framework serves as a 6-step proces...

Developing a process framework using principles of value‐based software engineering

AbstractIn this article we present a software process framework using the 4 + 1 theory and principles of value‐based software engineering (VBSE). The value‐based process framework serves as a 6‐step process guide, and explains critical interactions between the five theories in the 4 + 1 theory of value‐based software engineering. This article also applies the process framework to a supply chain organization through a case study analysis to illustrate its strength in practice. Copyright © 2007 John Wiley & Sons, Ltd.

How Much Software Quality Investment Is Enough: A Value-Based Approach

This article draws on results from the emerging field of value-based software engineering (VBSE). VBSE aims to provide a quantitative approach to questions as how much software quality investment is enough. Based on the COCOMO II cost-estimation model and the COQUALMO quality-estimation model, quantitative risk analysis helps determine when to stop testing software and release the product. Further, we show how the model and approach can assess the relative payoff of value-based testing as compared to value-neutral testing

Design decision rationale

Design decisions crucially influence the success of every software project. While the resulting design is typically documented quite well, the situation is usually different for the underlying rationale and decision-making process. Despite being recognized as a helpful approach in general, the explicit documentation of Design Decision Rationale (DDR) is not yet largely utilized due to some inhibitors (e.g., additional documentation effort). Experience with other qualities, e.g. software reusability, evidently shows that an improvement of these qualities only pays off on a large scale and therefore has to be pursued in a strategic, pre-planned, and carefully focused way. In this paper we argue that this also has to be considered for documenting DDR. To this end the paper presents: (i) the Decision, Goal, and Alternatives (DGA) DDR framework, (ii) experience in dealing with DGA, (iii) motivators and inhibitors of using DDR, and (iv) an approach for systematic DDR use that follows value-based software engineering principles.

Developing a theory of value-based software engineering

This paper presents an initial "4+1" theory of value-based software engineering (VBSE) that builds around the stakeholder win-win Theory W, and addresses the questions of "which values are important?" and "how is success assured?" for a given software engineering enterprise. The central Theory W then draws upon four additional theories - utility theory (how important are the values?), decision theory (how do stakeholders' values determine decisions?), dependency theory (how do dependencies affect value realization?), and control theory (how to adapt to change and control value realization?).