1. Cost-Benefit Analysis: Introduction and Overview 2. Project Appraisal - Principles 3. Project Appraisal - Decision-Rules 4. Private Cost-Benefit Analysis - Financial Appraisal 5. Cost-Benefit Analysis and Economic Efficiency 6. The Distribution of Project Net Benefits 7. Consumer and Producer Surplus in Cost-Benefit Analysis 8. Non-Market Valuation 9. Uncertainty, Information and Risk 10. Valuing Traded and Non-traded Goods in Cost-Benefit Analysis 11. Appraisal of the Distribution of Project Benefits and Costs 12. Economic Impact Analysis 13. Writing the Cost-Benefit Analysis Report

[What is a cost-benefit analysis?].

This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

Over the past decade, goal models have been used in Computer Science in order to represent software requirements, business objectives and design qualities. Such models extend traditional AI planning techniques for representing goals by allowing for partially defined and possibly inconsistent goals. This paper presents a formal framework for reasoning with such goal models. In particular, the paper proposes a qualitative and a numerical axiomatization for goal modeling primitives and introduces label propagation algorithms that are shown to be sound and complete with respect to their respective axiomatizations. In addition, the paper reports on preliminary experimental results on the propagation algorithms applied to a goal model for a US car manufacturer.

Reasoning with goal models

With modern requirements, there is an increasing tendency of considering multiple objectives/criteria simultaneously in many Software Engineering (SE) scenarios. Such a multi-objective optimization scenario comes with an important issue - how to evaluate the outcome of optimization algorithms, which typically is a set of incomparable solutions (i.e., being Pareto nondominated to each other). This issue can be challenging for the SE community, particularly for practitioners of Search-Based SE (SBSE). On one hand, multi-objective optimization could still be relatively new to SE/SBSE researchers, who may not be able to identify the right evaluation methods for their problems. On the other hand, simply following the evaluation methods for general multi-objective optimization problems may not be appropriate for specific SBSE problems, especially when the problem nature or decision maker's preferences are explicitly/implicitly known. This has been well echoed in the literature by various inappropriate/inadequate selection and inaccurate/misleading use of evaluation methods. In this paper, we first carry out a systematic and critical review of quality evaluation for multi-objective optimization in SBSE. We survey 717 papers published between 2009 and 2019 from 36 venues in seven repositories, and select 95 prominent studies, through which we identify five important but overlooked issues in the area. We then conduct an in-depth analysis of quality evaluation indicators/methods and general situations in SBSE, which, together with the identified issues, enables us to codify a methodological guidance for selecting and using evaluation methods in different SBSE scenarios.

pdf/how-to-evaluate-solutions-in-pareto-based-search-based-4jrei70g1c.pdf

How to Evaluate Solutions in Pareto-based Search-Based Software Engineering? A Critical Review and Methodological Guidance

Software projects have a high risk of cost and schedule overruns, which has been a source of concern for the software engineering community for a long time. One of the challenges in software project management is to make reliable prediction of delays in the context of constant and rapid changes inherent in software projects. This paper presents a novel approach to providing automated support for project managers and other decision makers in predicting whether a subset of software tasks (among the hundreds to thousands of ongoing tasks) in a software project have a risk of being delayed. Our approach makes use of not only features specific to individual software tasks (i.e. local data) -- as done in previous work -- but also their relationships (i.e. networked data). In addition, using collective classification, our approach can simultaneously predict the degree of delay for a group of related tasks. Our evaluation results show a significant improvement over traditional approaches which perform classification on each task independently: achieving 46% -- 97% precision (49% improved), 46% -- 97% recall (28% improved), 56% -- 75% F-measure (39% improved), and 78% -- 95% Area Under the ROC Curve (16% improved).

Predicting Delays in Software Projects Using Networked Classification (T)

Uncertainty complicates early requirements and architecture decisions and may expose a software project to significant risk. Yet software architects lack support for evaluating uncertainty, its impact on risk, and the value of reducing uncertainty before making critical decisions. We propose to apply decision analysis and multi-objective optimisation techniques to provide such support. We present a systematic method allowing software architects to describe uncertainty about the impact of alternatives on stakeholders' goals; to calculate the consequences of uncertainty through Monte-Carlo simulation; to shortlist candidate architectures based on expected costs, benefits and risks; and to assess the value of obtaining additional information before deciding. We demonstrate our method on the design of a system for coordinating emergency response teams. Our approach highlights the need for requirements engineering and software cost estimation methods to disclose uncertainty instead of hiding it.

/pdf/uncertainty-risk-and-information-value-in-software-34xh0jwjjq.pdf

Uncertainty, risk, and information value in software requirements and architecture

Sustainability management systems are important technologies used by large organisations to monitor and control their CO2 emissions and other environmental impacts. So far, these systems have been designed to support data collection and reporting mandated by regulations, but they provide little support for decision making in order to improve an organisation’s environmental sustainability. In this paper, we present a systematic method to help organisations make such decisions. Our method builds on goal-oriented requirements engineering for modelling an organisation’s sustainability goals and on statistical decision analysis for guiding decisions under uncertainty. By being explicit about uncertainty, our method provides a sound assessment of the risks associated with alternative options (including the “business as usual” option), and, through the concept of information value, informs decision makers whether reducing uncertainty through additional data collection will be useful for their decisions. We illustrate our approach on real sustainability decision questions at UCL’s Department of Computer Science.

/pdf/supporting-sustainability-decisions-in-large-organisations-5awvyvqv7b.pdf

Supporting Sustainability Decisions in Large Organisations

. Investment decisions by bank customers are increasingly linked to the demand for green investments. Without a meaningful life-cycle costing approach, the danger remains that both bank and customer are exposed to the risk of green washing. The same applies to lending decisions: here, the bank must ultimately assess the business model of the borrower as well as the subject of the loan. Without monetary integration of environmental indicators, the existing rating systems lose their ability to make accurate assessments of creditworthiness. Investment objects like real estate would be assessed with incorrect market values and, accordingly, the lending decision would be based on inadequate data. Based on this, the practice of lending is facing considerable adjustments.

Rating, Credit Decision and Pricing How Sustainability and Life Cycle Assessments are Changing Credit Practice

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Uncertainty, risk, and information value in software requirements and architecture

Supporting Sustainability Decisions in Large Organisations

Rating, Credit Decision and Pricing How Sustainability and Life Cycle Assessments are Changing Credit Practice