A Decision-Support System Approach to Economics-Driven Modularity Evaluation

doi:10.1016/B978-0-12-410464-8.00006-4

Economics-Driven Software Architecture

2014, Pages 105-128

https://doi.org/10.1016/B978-0-12-410464-8.00006-4 Get rights and content

Modularity debt is the most difficult kind of technical debt to quantify and manage. Modularity decay, thus modularity debt, causes huge losses over time in terms of reduced ability to provide new functionality and fix bugs, operational failures, and even canceled projects. As modularity debt accumulates over time, software system managers are often faced with a challenging task of deciding when and whether to refactor, for example, choosing to improve modularity or not. While the costs of refactoring are significant and immediate, their benefits are largely invisible, intangible, and long term. Existing research lacks effective methods to quantify the costs and benefits of refactoring to support refactoring decision making. In this chapter, we present a decision-support system (DSS) approach to the modularity debt management. Using such a system, managers would be able to play out various “what-if” scenarios to make informed decisions regarding refactoring. Our DSS approach is built on a scientific foundation for explicitly manifesting the economic implications of software refactoring activities so that the costs and benefits of such activities can be understood, analyzed, and predicted. We discuss our contributions and current progress in developing the building blocks and the underpinning framework, an integrated economics-driven modularization evaluation framework, for the modularity debt management decision-support system (MDM-DSS).

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Cited by (9)

Identification and analysis of the elements required to manage technical debt by means of a systematic mapping study
2017, Journal of Systems and Software
Citation Excerpt :
In fact, to identify technical debt items is usually the first step in managing technical debt (Curtis et al., 2012; Seaman et al., 2012). The identification of technical debt items can include establishing a list of the bad practices that create debt (Letouzey and Ilkiewicz, 2012; Marinescu, 2012) and identifying the potential kinds of technical debt from the sources of technical debt (Falessi et al., 2013), as well as determining the part of the system that must be refactored (Cai et al., 2014; Kazman et al., 2015). Therefore, there are many potential sources of technical debt at any time in any system (Brown et al., 2010).
Technical debt, a metaphor for the long-term consequences of weak software development, must be managed to keep it under control. The main goal of this article is to identify and analyze the elements required to manage technical debt. The research method used to identify the elements is a systematic mapping, including a synthesis step to synthesize the elements definitions. Our perspective differs from previous literature reviews because it focused on the elements required to manage technical debt and not on the phenomenon of technical debt or the activities used in performing technical debt management. Additionally, the rigor and relevance for industry of the current techniques used to manage technical debt are studied. The elements were classified into three groups (basic decision-making factors, cost estimation techniques, practices and techniques for decision-making) and mapped according three stakeholders’ points of view (engineering, engineering management, and business-organizational management). The definitions, classification, and analysis of the elements provide a framework that can be deployed to help in the development of models that are adapted to the specific stakeholders’ interests to assist the decision-making required in technical debt management and to assess existing models and methods. The analysis indicated that technical debt management is context dependent.
Manufacturing execution systems: A vision for managing software development
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Citation Excerpt :
An example of how this calculation would be formulated is given in the spreadsheet shown in Fig. 2, where the Monte Carlo simulations are performed by the @Risk software add-in. In this example project, the current code structure complexity increase factor is 9% and a proposed refactoring promises to bring the number back to 5% (Cai et al., 2014). Over the past 3 years we have used this infrastructure to collect data from, and to analyze, more than 20 open source projects and 1 proprietary project.
Software development suffers from a lack of predictability with respect to cost, time, and quality. Predictability is one of the major concerns addressed by modern manufacturing execution systems (MESs). A MES does not actually execute the manufacturing (e.g., controlling equipment and producing goods), but rather collects, analyzes, integrates, and presents the data generated in industrial production so that employees have better insights into processes and can react quickly, leading to predictable manufacturing processes. In this paper, we introduce the principles and functional areas of a MES. We then analyze the gaps between MES-vision-driven software development and current practices. These gaps include: (1) lack of a unified data collection infrastructure, (2) lack of integrated people data, (3) lack of common conceptual frameworks driving improvement loops from development data, and (4) lack of support for projection and simulation. Finally, we illustrate the feasibility of leveraging MES principles to manage software development, using a Modularity Debt Management Decision Support System prototype we developed. In this prototype we demonstrate that information integration in MES-vision-driven systems enables new types of analyses, not previously available, for software development decision support. We conclude with suggestions for moving current software development practices closer to the MES vision.
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Chapter 6 - A Decision-Support System Approach to Economics-Driven Modularity Evaluation