Irit Hadar

The role of domain knowledge in requirements elicitation via interviews: an exploratory study

Requirements elicitation is the first activity in the requirements engineering process. It includes learning, surfacing, and discovering the requirements of the stakeholders of the developed system. Various elicitation techniques exist to help analysts elicit the requirements from the different stakeholders; the most commonly used technique is the interview. Analysts may have domain knowledge prior to the elicitation process. Such knowledge is commonly assumed to have positive effects on requirements engineering processes, in that it fosters communication, and a mutual understanding of the needs. However, to a minor extent, some negative effects have also been reported. This paper presents an empirical study in which the perceived and actual effects of prior domain knowledge on requirements elicitation via interviews were examined. The results indicate that domain knowledge affects elicitation via interview in two main aspects: communication with the customers and understanding their needs. The findings provide insights as to both the positive and negative effects of domain knowledge on requirements elicitation via interview, as perceived by participants with and without domain knowledge, and show the existence of an actual effect on the course of the interviews. Furthermore, these insights can be utilized in practice to support analysts in the elicitation process and to form requirements analysis teams. They highlight the different contributions that can be provided by analysts with different levels of domain knowledge in requirements analysis teams and the synergy that can be gained by forming heterogeneous teams of analysts.

Applying ontology-based rules to conceptual modeling: a reflection on modeling decision making

Conceptual modeling represents a domain independently of implementation considerations for purposes of understanding the problem at hand and communicating about it. However, different people may construct different models given the same domain. Variations among correct models, while known and familiar in practice, have hardly been investigated in the literature. Their roots are in the decisions made during the modeling process, where modelers face the need to map reality into modeling constructs. This paper reports an empirical study whose aim is to explore model variations and in particular to examine possible directions for reducing them. Specifically, the study uses a multimethod research paradigm to examine the effect of applying ontology-based modeling rules on modeling decisions as reflected in resulting model variations. The findings of the study provide insights into the variations phenomenon, as well as to the application of ontology-based modeling rules.

Understanding Declare models: strategies, pitfalls, empirical results

Declarative approaches to business process modeling are regarded as well suited for highly volatile environments, as they enable a high degree of flexibility. However, problems in understanding and maintaining declarative process models often impede their adoption. Likewise, little research has been conducted into the understanding of declarative process models. This paper takes a first step toward addressing this fundamental question and reports on an empirical investigation consisting of an exploratory study and a follow-up study focusing on the system analysts’ sense-making of declarative process models that are specified in Declare. For this purpose, we distributed real-world Declare models to the participating subjects and asked them to describe the illustrated process and to perform a series of sense-making tasks. The results of our studies indicate that two main strategies for reading Declare models exist: either considering the execution order of the activities in the process model, or orienting by the layout of the process model. In addition, the results indicate that single constraints can be handled well by most subjects, while combinations of constraints pose significant challenges. Moreover, the study revealed that aspects that are similar in both imperative and declarative process modeling languages at a graphical level, while having different semantics, cause considerable troubles. This research not only helps guiding the future development of tools for supporting system analysts, but also gives advice on the design of declarative process modeling notations and points out typical pitfalls to teachers and educators of future systems analysts.

Uncovering cultural perceptions and barriers during knowledge audit

Purpose – The purpose of this paper is to introduce and illustrate the importance of uncovering tacit perceptions during knowledge management (KM) audit, in order to identify cultural barriers that may interfere with KM initiatives. The goal of such KM initiatives is to leverage a firms capacity to efficiently produce value from knowledge held by employees and embedded in processes. Current audit practices analyze the explicit information gained through interviews and questionnaires, focusing on the organizations culture, existing KM processes and the improved KM processes it wishes to implement. The paper seeks to suggest an approach for uncovering and analyzing tacit perceptions identified through interviews and discussions as an inherent part of KM audit.Design/methodology/approach – The research was conducted during a KM audit in a large international software development organization. The research methodology was composed of two disciplines. The first, used for data collection, was a knowledge‐engi...

How intuitive is object-oriented design?

Intuition is a powerful tool that helps us navigate through life, but it can get in the way of more formal processes.

Controversy Corner: Why and how can human-related measures support software development processes?

In this paper we discuss why and how measures related to human aspects should be incorporated into software development processes. This perspective is based on the vast evidence that human aspects are the source of the majority of problems associated with software development projects. Having said that, we do not blame the humans involved in software development processes; rather, we suggest that human-related measures might be one means by which human aspects of software development processes can be supported.

On the Contribution of UML Diagrams to Software System Comprehension

Program comprehension has been researched extensively ever since software systems became complex and longer than a few hundred code lines. At the same time, the way in which people comprehend visual models of software systems has received much less attention. This paper focuses on the comprehension of UML diagrams. During the research presented in this paper, data was gathered from the work of two groups. Group 1 consisted of 13 senior computer science students who worked in five teams. The students were asked to trace and analyze the process by which they retrieved information from UML diagrams of a given system. Group 2 consisted of 42 senior computer science students who were requested to complete a questionnaire in which they were asked to rank different types of UML diagrams according to their importance. The section on data analysis discusses strategies adopted by the novices in their attempt to reveal the meaning of a set of UML diagrams, as well as their attitudes towards the different diagrams. One of the interesting observations is that although each team had its own preferences with respect to the usefulness of each specific type of diagrams, the overall use of each diagram type is very similar across the teams.

An empirical study of requirements model understanding: Use Case vs. Tropos models

Visual modelling languages are commonly used to support software requirements analysis and documentation. A variety of languages are available, based on different conceptual paradigms. They can be roughly divided into two main groups: goal-oriented approaches and scenario-based approaches. In the last ten years, numerous works developed case studies that illustrate the effectiveness and limitations of goal-oriented and scenario-based approaches. A few works even suggest coupling these approaches in order to capture requirements from different perspectives. However, experimental comparisons of these approaches have been rarely addressed. This paper presents the design and preliminary results of an empirical study that compares two state of the art requirements modelling methods: Use Cases, which is a scenario-based approach, and Tropos, which is a goal-oriented approach. The objective is to evaluate different levels of comprehension of requirements models expressed in both methods, as well as to estimate the time required to perform simple analysis tasks using both methods. Preliminary results show that Tropos models seem to be more comprehensible, although more time consuming, than Use Case models to novice requirements analysts.

Making Sense of Declarative Process Models: Common Strategies and Typical Pitfalls

Declarative approaches to process modeling are regarded as well suited for highly volatile environments as they provide a high degree of flexibility. However, problems in understanding and maintaining declarative business process models impede often their usage. In particular, how declarative models are understood has not been investigated yet. This paper takes a first step toward addressing this question and reports on an exploratory study investigating how analysts make sense of declarative process models. We have handed out real-world declarative process models to subjects and asked them to describe the illustrated process. Our qualitative analysis shows that subjects tried to describe the processes in a sequential way although the models represent circumstantial information, namely, conditions that produce an outcome, rather than a sequence of activities. Finally, we observed difficulties with single building blocks and combinations of relations between activities.

When intuition and logic clash: The case of the object-oriented paradigm

Abstract This paper presents difficulties illustrated by software development practitioners with regard to the use and understanding of concepts and principles of the object-oriented paradigm. In order to understand these difficulties and the sources from which they stem, a theoretical framework borrowed from cognitive psychology, called the dual-process theory, was used. The empirical findings, collected using tools based on the qualitative research approach and analyzed through the lens of the dual-process theory, suggest that a tension between intuitive and logical thinking modes may lead to simple mistakes software engineers exhibit when practicing object-oriented analysis and design (OOA/D). Examining the understanding of OOA/D from this perspective helps explain some of the previously documented, as well as some newly identified difficulties in learning and practicing OOA/D, as well as guide the design of future quantitative experiments to understand how prevalent these phenomena are.