Software Process Improvement and Human Judgement Heuristics

This paper exemplifies how better knowledge about human judgement strategies known as heuristics can be used to improve software processes, especially estimation and prediction processes. Human judgement heuristics work well when they exploit a fit between their structure and the structure of the environment in which they are used. This use of environmental fit may lead to amazingly good judgements based on little information and simple computations compared with more formal approaches. Sometimes, however, the heuristics may lead to poor judgements. Knowing more about the strengths and weaknesses of human judgement heuristics we may be able to (1) know when to use formal process improvement approaches and when to use less expensive expert judgements, (2) support the experts in situations where the experts’ judgements strategies are known to perform poorly, (3) improve the formal processes with elements from the experts’ strategies, and (4) train the experts in the use of more optimal judgement strategies. A small-scale experiment was carried out to evaluate the use of the representativeness heuristic in a software development effort estimation context. The results indicate that the actual use of the representativeness heuristic differed very much among the estimators and was not always based on an awareness of fit between the structure of the heuristic and the structure of the environment. Estimation strategies only appropriate in low uncertainty development environments were used in high uncertainty environments. A possible consequence of this finding is that expert estimators should be trained in assessing how well previous software projects predict new software projects, i.e., the uncertainty of the environment, and how this uncertainty should impact the estimation strategy

Designing IS service strategy: an information acceleration approach

Information technology-based innovation involves considerable risk that requires insight and foresight. Yet, our understanding of how managers develop the insight to support new breakthrough applications is limited and remains obscured by high levels of technical and market uncertainty. This paper applies a new experimental method based on “discrete choice analysis” and “information acceleration” to directly examine how decisions are made in a way that is behaviourally sound. The method is highly applicable to information systems researchers because it provides relative importance measures on a common scale, greater control over alternate explanations and stronger evidence of causality. The practical implications are that information acceleration reduces the levels of uncertainty and generates a more accurate rationale for IS service strategy decisions

Framing purposeful evaluation through critical systems thinking

Two traditions of practice – evaluation and systems – share three significant concerns regarding development intervention: (i) making sense of complex interrelationships and the continual change brought about by such relationships; (ii) engaging with multiple (including exogenous and endogenous), often conflicting, perspectives on situations; and (iii) challenging vicious cycles of practice and understanding by cultivating a more radical learning culture. These challenges might be described successively in terms of cultivating a shift from (i) summative to formative evaluation (ii) positional bargaining to interest based negotiation, and (iii) purposive to purposeful action. Some ideas from traditions of social learning and critical systems thinking are presented to support a re-framing of intervention and evaluation from one serving the 'project state' towards one serving more radical transformative practice

Autonomous power system: Integrated scheduling

The Autonomous Power System (APS) project at NASA Lewis Research Center is designed to demonstrate the abilities of integrated intelligent diagnosis, control and scheduling techniques to space power distribution hardware. The project consists of three elements: the Autonomous Power Expert System (APEX) for fault diagnosis, isolation, and recovery (FDIR), the Autonomous Intelligent Power Scheduler (AIPS) to determine system configuration, and power hardware (Brassboard) to simulate a space-based power system. Faults can be introduced into the Brassboard and in turn, be diagnosed and corrected by APEX and AIPS. The Autonomous Intelligent Power Scheduler controls the execution of loads attached to the Brassboard. Each load must be executed in a manner that efficiently utilizes available power and satisfies all load, resource, and temporal constraints. In the case of a fault situation on the Brassboard, AIPS dynamically modifies the existing schedule in order to resume efficient operation conditions. A database is kept of the power demand, temporal modifiers, priority of each load, and the power level of each source. AIPS uses a set of heuristic rules to assign start times and resources to each load based on load and resource constraints. A simple improvement engine based upon these heuristics is also available to improve the schedule efficiency. This paper describes the operation of the Autonomous Intelligent Power Scheduler as a single entity, as well as its integration with APEX and the Brassboard. Future plans are discussed for the growth of the Autonomous Intelligent Power Scheduler

Clinical experience as evidence in evidence-based practice

Background. This paper's starting point is the recognition (descriptive not normative) that, for the vast majority of day-to-day clinical decision-making situations, the 'evidence' for decision-making is experiential knowledge. Moreover, reliance on this knowledge base means that nurses must use cognitive shortcuts or heuristics for handling information when making decisions. These heuristics encourage systematic biases in decision-makers and deviations from the normative rules of 'good' decision-making. Aims. The aim of the paper is to explore three common heuristics and the biases that arise when handling complex information in clinical decision-making (overconfidence, hindsight and base rate neglect) and, in response to these biases, to illustrate some simple techniques for reducing the negative influence of heuristics. Discussion. Nurses face a limited range of types of uncertainty in their clinical decisions and draw primarily on experiential knowledge to handle these uncertainties. This paper argues that experiential knowledge is a necessary but not sufficient basis for clinical decision-making. It illustrates how overconfidence in one's knowledge base, being correct 'after the event' or with the benefit of hindsight, and ignoring the base rates associated with events, conditions or health states, can impact on professional judgements and decisions. The paper illustrates some simple strategies for minimizing the impact of heuristics on the real-life clinical decisions of nurses. Conclusion. The paper concludes that more research knowledge of the impact of heuristics and techniques to combat them in nursing decisions is needed

A heuristics approach for computing the largest eigenvalue of a pairwise comparison matrix

Pairwise comparison matrices (PCMs) are widely used to capture subjective human judgements, especially in the context of the Analytic Hierarchy Process (AHP). Consistency of judgements is normally computed in AHP context in the form of consistency ratio (CR), which requires estimation of the largest eigenvalue (Lmax) of PCMs. Since many of these alternative methods do not require calculation of eigenvector, Lmax and hence the CR of a PCM cannot be easily estimated. We propose in this paper a simple heuristics for calculating Lmax without any need to use Eigenvector Method (EM). We illustrated the proposed procedure with larger size matrices. Simulation is used to compare the accuracy of the proposed heuristics procedure with actual Lmax for PCMs of various sizes. It has been found that the proposed heuristics is highly accurate, with errors less than 1%. The proposed procedure would avoid biases and help managers to make better decisions. The advantage of the proposed heuristics is that it can be easily calculated with simple calculations without any need for specialised mathematical procedures or software and is independent of the method used to derive priorities from PCMs

Cognitive barriers during monitoring-based commissioning of buildings

Monitoring-based commissioning (MBCx) is a continuous building energy management process used to optimize energy performance in buildings. Although monitoring-based commissioning (MBCx) can reduce energy waste by up to 20%, many buildings still underperform due to issues such as unnoticed system faults and inefficient operational procedures. While there are technical barriers that impede the MBCx process, such as data quality, the focuses of this paper are the non-technical, behavioral and organizational, barriers that contribute to issues initiating and implementing MBCx. In particular, this paper discusses cognitive biases, which can lead to suboptimal outcomes in energy efficiency decisions, resulting in missed opportunities for energy savings. This paper provides evidence of cognitive biases in decisions during the MBCx process using qualitative data from over 40 public and private sector organizations. The results describe barriers resulting from cognitive biases, listed in descending order of occurrence, including: risk aversion, social norms, choice overload, status quo bias, information overload, professional bias, and temporal discounting. Building practitioners can use these results to better understand potential cognitive biases, in turn allowing them to establish best practices and make more informed decisions. Researchers can use these results to empirically test specific decision interventions and facilitate more energy efficient decisions

Investigating heuristic evaluation as a methodology for evaluating pedagogical software: An analysis employing three case studies

This paper looks specifically at how to develop light weight methods of evaluating pedagogically motivated software. Whilst we value traditional usability testing methods this paper will look at how Heuristic Evaluation can be used as both a driving force of Software Engineering Iterative Refinement and end of project Evaluation. We present three case studies in the area of Pedagogical Software and show how we have used this technique in a variety of ways. The paper presents results and reflections on what we have learned. We conclude with a discussion on how this technique might inform on the latest developments on delivery of distance learning. © 2014 Springer International Publishing

Personalised correction, feedback, and guidance in an automated tutoring system for skills training

In addition to knowledge, in various domains skills are equally important. Active learning and training are effective forms of education. We present an automated skills training system for a database programming environment that promotes procedural knowledge acquisition and skills training. The system provides support features such as correction of solutions, feedback and personalised guidance, similar to interactions with a human tutor. Specifically, we address synchronous feedback and guidance based on personalised assessment. Each of these features is automated and includes a level of personalisation and adaptation. At the core of the system is a pattern-based error classification and correction component that analyses student input

Trends in the Research on Software Process Improvement in Scandinavia

Abstract not available