The impact of domain knowledge on the effectiveness of requirements engineering activities

Niknafs, Ali; Berry, Daniel M.

doi:10.1007/s10664-015-9416-2

Cited by 21 publications

(30 citation statements)

References 40 publications

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“…McAllister conjectures that this may be due to the fact that analysts assume that they know the needs of the customers and, consequently, specify the system that they visualize rather than the system that users really want. In a similar vein, Niknafs and Berry [16], [20] state that although profound domain knowledge eases the understanding of details of the problem, it may also encourage analysts to make assumptions about the requirements. Pitts and Browne [12] are of the same opinion.…”

Section: Introductionmentioning

confidence: 99%

“…Niknafs and Berry [4], [20] conducted a controlled experiment aimed at studying the impact of domain knowledge and industrial and requirements engineering experience on elicitation effectiveness. The elicitation technique in this case was brainstorming.…”

Section: Introductionmentioning

confidence: 99%

“…Niknafs and Berry [16], [20] conducted another controlled experiment with industry professionals. Eight subjects participated in the study; four of the subjects were employed as developers at a company whose name has been omitted (company C), whereas the other four were from the University of Waterloo.…”

Section: Introductionmentioning

confidence: 99%

“…Niknafs [20] reports two controlled experiments (E1 and E2) with computer science and software engineering students. E1 is the experiment published in [4], and E2 is an internal and exact replication (same procedure, same problem domains, same evaluation process, etc.)…”

Section: Introductionmentioning

confidence: 99%

“…The goal of E2 is to increase the sample size used in E1, as well as to improve the balance between the mix of familiarities (i.e., number of DI and DA subjects that belong to each team). E2 [4], [20] Niknafs and Berry [16], [20] Niknafs [20] Hadar et al [5] Kristensson et al [7] Controlled Experiment is analysed in [20] together with E1, as reported below. The results do not reveal that the mix of familiarities has any significant effect on any of the tested response variables (number of raw, relevant, feasible and innovative ideas).…”

Section: Introductionmentioning

confidence: 99%

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Effect of Domain Knowledge on Elicitation Effectiveness: An Internally Replicated Controlled Experiment

Aranda

Dieste

Juristo

2016

IIEEE Trans. Software Eng.

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Abstract-Context. Requirements elicitation is a highly communicative activity in which human interactions play a critical role. A number of analyst characteristics or skills may influence elicitation process effectiveness. Aim. Study the influence of analyst problem domain knowledge on elicitation effectiveness. Method. We executed a controlled experiment with post-graduate students. The experimental task was to elicit requirements using open interview and consolidate the elicited information immediately afterwards. We used four different problem domains about which students had different levels of knowledge. Two tasks were used in the experiment, whereas the other two were used in an internal replication of the experiment; that is, we repeated the experiment with the same subjects but with different domains. Results. Analyst problem domain knowledge has a small but statistically significant effect on the effectiveness of the requirements elicitation activity. The interviewee has a big positive and significant influence, as does general training in requirements activities and interview experience. Conclusion. During early contacts with the customer, a key factor is the interviewee; however, training in tasks related to requirements elicitation and knowledge of the problem domain helps requirements analysts to be more effective. 1INTRODUCTIONR EQUIREMENTS elicitation, that is, seeking, capturing and consolidating requirements, is a core activity of any requirements engineering process [1] and has a direct influence on software quality [2]. Requirements elicitation depends on intensive communication between users and analysts in order to gather the right information [3]. Human interactions play an important role in this context. On one hand, customers should be able to interact and communicate their needs to analysts. On the other hand, analysts should be able to draw out and grasp the necessary domain information from customers.The effectiveness of requirements engineering activities is believed to partially depend on the participating individuals [4]. It has been observed that interview effectiveness can vary significantly depending on interviewer skills, probably because proficiency affects the course of the questioning [5]. As a result, elicitation strongly depends on the individual doing the interviewing [6]. Similar effects have been identified in brain-storming [4] and using other elicitation techniques [7].Several personal attributes may have a bearing on the effectiveness of any requirements-related task: experience [8]

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Effect of Domain Knowledge on Elicitation Effectiveness: An Internally Replicated Controlled Experiment

Aranda

Dieste

Juristo

2016

IIEEE Trans. Software Eng.

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Guidelines for Conducting Software Engineering Research

Stol

Fitzgerald

2020

Contemporary Empirical Methods in Software Engineering

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Who Is Behind the Model? Classifying Modelers Based on Pragmatic Model Features

Burattin

Soffer

Fahland

et al. 2018

Lecture Notes in Computer Science

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Process modeling tools typically aid end users in generic, non-personalized ways. However, it is well conceivable that different types of end users may profit from different types of modeling support. In this paper, we propose an approach based on machine learning that is able to classify modelers regarding their expertise while they are creating a process model. To do so, it takes into account pragmatic features of the model under development. The proposed approach is fully automatic, unobtrusive, tool independent, and based on objective measures. An evaluation based on two data sets resulted in a prediction performance of around 90%. Our results further show that all features can be efficiently calculated, which makes the approach applicable to online settings like adaptive modeling environments. In this way, this work contributes to improving the performance of process modelers.

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The impact of domain knowledge on the effectiveness of requirements engineering activities

Cited by 21 publications

References 40 publications

Effect of Domain Knowledge on Elicitation Effectiveness: An Internally Replicated Controlled Experiment

Effect of Domain Knowledge on Elicitation Effectiveness: An Internally Replicated Controlled Experiment

Guidelines for Conducting Software Engineering Research

Who Is Behind the Model? Classifying Modelers Based on Pragmatic Model Features

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