Bayesian networks for enhancement of requirements engineering: a literature review

Abstract

Requirements analysis is the software engineering stage that is closest to the users' world. It also involves tasks that are knowledge intensive. Thus, the use of Bayesian networks (BNs) to model this knowledge would be a valuable aid. These probabilistic models could manage the imprecision and ambiguities usually present in requirements engineering (RE). In this work, we conduct a literature review focusing on where and how BNs are applied on subareas of RE in order to identify which gaps remain uncovered and which methods might engineers employ to incorporate this intelligent technique into their own requirements processes. The scarcity of identified studies (there are only 20) suggests that not all RE areas have been properly investigated in the literature. The evidence available for adopting BNs into RE is sufficiently mature yet the methods applied are not easily translatable to other topics. Nonetheless, there are enough studies supporting the applicability of synergistic cooperation between RE and BNs. This work provides a background for understanding the current state of research encompassing RE and BNs. Functional, non-functional and -ilities requirements artifacts are enhanced by the use of BNs. These models were obtained by interacting with experts or by learning from databases. The most common criticism from the point of view of BN experts is that the models lack validation, whereas requirements engineers point to the lack of a clear application method for BNs and the lack of tools for incorporating them as built-in help functions.

Textual analysis of defects in engineering documentation via systematic decision execution

“…A predictive model was developed to verify that system performance is efficiently forecasted using utility measurements for the calculated predictor variable of Ambiguity. Though not comprehensive, this characterizes the most common defective attribute in the requirements statements . The regression prediction defined the relationship among the predictor variable Ambiguity ( x ), and the response variable System Performance ( y ).…”

“…SE processes reflect the unique needs of the program, product, or system that is being developed. The Department of Defense (DoD) defines SE as “an interdisciplinary approach, encompassing the entire technical effort to evolve and verify an integrated and total life cycle balanced set of system, people, and process solutions that satisfy customer needs.” In status reporting and assessment of SE process, metrics deliver greater visibility of the performance of the “system that produces the system.” These metrics supplement the cost and schedule control measures discussed in our research. SE process metrics quantify the effectiveness and productivity of the SE.…”

“…In addition, there is evidence to suggest that existing methods and frameworks often result in poorly defined and unsatisfactory requirements . The RE process can identify attributes relating to linguistic quality and subsequently assess the correct‐system utility through textual analysis of requirement documents, thus enabling data‐driven decision analysis to proceed to development or placement in operational environments .…”

Using Bayesian Networks to estimate Strategic Indicators in the context of Rapid Software Development

“…As far as we know, no previous study has proposed a general methodology for defining SIs prediction models within the context of RSD [2]. However, there exist several studies proposing individual SIs using BNs in the area of software engineering, specifically, to estimate "teamwork quality" [3], to model quality in software projects [4], to estimate effort in software development [5], for requirements engineering [6], to predict software defects [7], and to estimate value and help decision making [8,9]. The difference between these works and ours is that we build a SI BN prediction model based upon companies' business knowledge and data automatically measured from heterogeneous data sources, and within the context of RSD.…”

Data-Driven Supervision of Autonomous Systems

“…While early work characterized the design-time assumptions of a requirements model [16], no practical run-time approach has been proposed to provide their probabilistic evaluation. Bayesian Networks (BNs) have been widely employed in Requirements Engineering for a variety of tasks [115], including the run-time verification of requirements [141]. Wu et al [299] propose a preliminary study of the relationship between an iStar [101] requirements model and BNs in the context of requirements elicitation.…”