Differences between Novice and Expert Systems Analysts: What Do We Know and What Do We Do?

Schenk, Karin; Vitalari, Nicholas P.; Davis, K. Shannon

doi:10.1080/07421222.1998.11518195

Cited by 146 publications

(103 citation statements)

References 55 publications

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“…The experiment done by Burkhardt et al [16] indicated that novices spent less time than experts, even if the difference was not significant. Also, Schenk et al [17] found that experts spent more time than novices when performing analysis tasks. These results are similar to our findings because the design process includes analysis activities [14] 3 .…”

Section: B Rq2: Experts Vs Novices 1) Resultsmentioning

confidence: 99%

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Professional status and expertise for UML class diagram comprehension: An empirical study

Soh

Sharafi

Plas³

et al. 2012

2012 20th IEEE International Conference on Program Comprehension (ICPC)

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Abstract-Professional experience is one of the most important criteria for almost any job offer in software engineering. Professional experience refers both to professional status (practitioner vs. student) and expertise (expert vs. novice). We perform an experiment with 21 subjects including both practitioners and students, and experts and novices. We seek to understand the relation between the speed and accuracy of the subjects and their status and expertise in performing maintenance tasks on UML class diagrams. We also study the impact of the formulation of the maintenance task. We use an eye-tracking system to gather the fixations of the subjects when performing the task. We measure the subjects' comprehension using their accuracy, the time spent, the search effort, the overall effort, and the question comprehension effort. We found that (1) practitioners are more accurate than students while students spend around 35 percent less time than practitioners, (2) experts are more accurate than novices while novices spending around 33 percent less time than experts, (3) expertise is the most important factor for accuracy and speed, (4) experienced students are more accurate and spend around 37 percent less time than experienced practitioners, and (5) when the description of the task is precise, the novice students can be accurate. We conclude that it is an illusion for project managers to focus on status only when recruiting a software engineer. Our result is the starting point to consider the differences between status and expertise when studying software engineers' productivity. Thus, it can help project managers to recruit productive engineers and motivated students to acquire the experience and ability in the projects.

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Section: B Rq2: Experts Vs Novices 1) Resultsmentioning

confidence: 99%

“…Schenk et al [17] used verbal reports to examine the difference in the requirements analysis process between experts and novices. Novices were identified using the years of experience and the experts were categorized with the rating scale of their supervisors.…”

Section: A Experts and Novices Comparisonmentioning

confidence: 99%

Professional status and expertise for UML class diagram comprehension: An empirical study

Soh

Sharafi

Plas³

et al. 2012

2012 20th IEEE International Conference on Program Comprehension (ICPC)

View full text Add to dashboard Cite

show abstract

“…In contrast, novice analysts commented that it would not usually occur to them to think about stakeholders' VME, until there were problems in their project, while expert analysts described taking time to reflect on stakeholders' VME. In similar studies in the literature, major differences in problem identification skills and political and social awareness between novice and expert analysts have been reported [35,36], as have the need for improved analyst communication skills and the importance of rapport building [1,26].…”

Section: Current Analysis Practicementioning

confidence: 99%

Value-based requirements engineering: method and experience

Thew

Sutcliffe

2017

Requirements Eng

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'Socio-political' issues, such as emotions, values and people's feelings, are often cited as problems in the RE process. A method is described for analysing such issues. The method consists of a taxonomy of stakeholders' values, motivations and emotions (VME), with process guidance for eliciting and analysing these issues for the RE process and design implications. Values are personal attitudes or long-term beliefs which may influence stakeholder functional and non-functional requirements. Motivations are psychological constructs related to personality traits which may be viewed as stakeholders' long-term goals in RE. Emotions are cues to stakeholders' reactions arising from value/motivation conflicts. The method is supported by a website which illustrates the taxonomy with explanations and scenarios describing problems arising from value conflicts, and from poor understanding of stakeholder values. Two method validation studies were undertaken: first, an evaluation of the website and method by novices and RE experts; and second, case study applications of RE value analysis in real-world industrial practice. The method was used by all practitioners, although in different ways, some used it to create an agenda of issues for analysis while others employed the VMEs to interpret stakeholders' views and manage stakeholder negotiations. The validation studies provide evidence for the acceptability of the method for industrial practitioners, illustrating how valuerelated problems are identified and analysed effectively by the method. The utility of analysing VMEs is compared to other 'socio-political issues'-oriented methods in RE and methods which focus on monetized values in product requirements.

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“…Experienced modelers have better capabilities in terms of understanding domain-specific knowledge, structuring problems and dealing with cognitive difficulties [19]. Novice modelers are apt to avoid doing in-depth examination thus resulting in insufficient distinction among concepts [5].…”

Section: Hypothesismentioning

confidence: 99%

“…Novice modelers are apt to avoid doing in-depth examination thus resulting in insufficient distinction among concepts [5]. Schenk et al [19] reveal that novice modelers gain fewer domain-specific concepts than experienced ones and overemphasize general issues instead of functional requirements and focused information issues.…”

Section: Hypothesismentioning

confidence: 99%

Study on the Use of the Constant Comparison Method : Lessons from Training Novice Modelers

Kim¹,

Park

Rho³

2013

The Journal of Society for e-Business Studies

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Conceptual modeling is a critical activity for developing successful business information systems. The objective of this study is to evaluate the possibility of applying the constant comparison method from the grounded theory to conceptual modeling. To achieve the objective, we trained novice modelers and split them into two groups for evaluation. The experimental results show that applying the constant comparison method could increase acceptability from more experienced conceptual modelers. Moreover, while the control group was experienced difficulties when domain knowledge is unfamiliar, the experimental group could handle difficulties more effectively. In addition, applying the constant comparison method also decreased the time to complete analysis for conceptual modeling.

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Differences between Novice and Expert Systems Analysts: What Do We Know and What Do We Do?

Cited by 146 publications

References 55 publications

Professional status and expertise for UML class diagram comprehension: An empirical study

Professional status and expertise for UML class diagram comprehension: An empirical study

Value-based requirements engineering: method and experience

Study on the Use of the Constant Comparison Method : Lessons from Training Novice Modelers

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