Semi-automation for ambiguity resolution in Open Source Software requirements

Gill, Kanwal Daud; Raza, Arif; Zaidi, Athar Mohsin; Kiani, Muhammad Muneeb

doi:10.1109/ccece.2014.6900955

Cited by 11 publications

(6 citation statements)

References 18 publications

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“…Thus, requirements engineers have to go through these reports, discriminate users' intents and decide on whether to accept or reject. However, such process of understanding, classifying and clarifying end users' enhancement reports becomes a tedious and labor-intensive task, even for experienced requirements engineers 27 .…”

Section: Overviewmentioning

confidence: 99%

Utilizing Creator Profiles for Predicting Valuable User Enhancement Reports

Niu

Chen

et al. 2023

Preprint

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Users of software applications use Issue Tracking Systems (ITSs) to file enhancement reports, which leads to a large quantity of user requests. Indeed, these reports have become an important source for software requirements because they help to continuously improve software applications. Usually, developers and maintainers evaluate them and decide which user reports can be accepted. However, enhancement reports are continuously being raised one after another, which makes this process time-consuming and labor-intensive. Timely handling and implementation of these enhancement reports can effectively improve user satisfaction and product competitiveness. Thus, research has focused on automated methods for predicting which enhancement reports are likely to be approved, to maximum the value derived from user reports. However, reported results of existing approaches are typically not good enough for practical use. In this paper, we propose a novel creator-profile-based method to explore dependencies among enhancements to improve the prediction performance. Firstly, we define the concept of a creator profile, including a general method of how to generate creator profiles from the data set. Then we explain how to employ creator profiles to the problem of enhancement report approval prediction. Finally, we evaluate our approach on 40,551 enhancement reports collected from ITSs. The experimental results indicate that our proposed approach greatly improve on existing state of the art, especially in predicting approved reports. For cross-application prediction, the accuracy is 80.7%, while for non-cross-application prediction, the overall accuracy is 83.6%. That is, with the proposed approach, over 80% of user requests can be automatically identified for exacting valuable user requirements, which significantly reduces labor costs. Replication package is available at: https://anonymous.4open.science/r/approval_prediction-507E

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Section: Overviewmentioning

confidence: 99%

Utilizing Creator Profiles for Predicting Valuable User Enhancement Reports

Niu

Chen

et al. 2023

Preprint

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“…The authors propose the detection and resolution of ambiguity problems before documenting, through the adoption of natural language processing (NLP), the semantic web technique and the measurement of the similarity between requirements.  S14 - Gill et al (2014) Requirements": This research proposes the implementation of an ontologybased intelligent agent (OBIA) in order to determine metric information in software requirements. Some of the actions permitted by the intelligent agent are: Partially deleting the person from the information processing processes, avoiding the loss of information and enhancing the quantity of metric information in the requirements recompilation phase, or improving software quality.…”

Section: Extraction Of Informationmentioning

confidence: 99%

Building an integrated requirements engineering process based on Intelligent Systems and Semantic Reasoning on the basis of a systematic analysis of existing proposals

Corral¹,

Sanchez²,

Antonelli³

2022

jucs

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Requirements Engineering is one of the fundamental activities in the software development process and is oriented toward what should be produced. One of the development team’s most common problems is a lack of communication regarding an understanding of the discourse domain and how to integrate and process excessive information originating from different sources. This may lead to errors of omission and the consequent production of incomplete and inconsistent artifacts, which will have a direct effect on the quality of the software. The use of machine learning techniques helps the development team produce successful software on the basis of the acquisition of knowledge and human experience with which to understand the domain of the application. This paper, therefore, presents a proposal for a new methodological process oriented toward the construction of a vocabulary concerning the application domain. The authors propose to do this by employing Natural Language Processing (NLP), ontologies and heuristics that will lead to the production of a Lexicon that is common to analysts and customers, both of whom will understand the universe of discourse, thus mitigating problems of completeness. This objective has been achieved by carrying out a Systematic Literature Review of the artificial intelligence techniques employed in the requirements engineering process, which led to the discovery that 41.37% use NLP, while 55.71% apply ontologies such as semantic reasoners which help solve the problem of language ambiguity, the structures in specifications or the identification of key concepts with which to establish traceability links. However, the review also showed that the problems regarding the comprehension and completeness of requirements problems have yet to be resolved.

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“…An example of ambiguity correction is the work of Gill et al [9], who proposed a framework to develop semi-automatic tools for ambiguity correction in open source software requirements. They discussed some challenges in open source requirements that make it a special case.…”

Section: Ambiguity Correctionmentioning

confidence: 99%

“… Ambiguity correction: refers to semi-automated tools that interact with the user to make the needed corrections such as the work of Gill et al [9].…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Formal Software Requirements Ambiguity Prevention Tool

Alomari¹,

Elazhary²

2018

ijacsa

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Abstract-The success of the software engineering process depends heavily on clear unambiguous software requirements. Ambiguity refers to the possibility to understand a requirement in more than one way. Unfortunately, ambiguity is an inherent property of the natural languages used to write the software user requirements. This could cause a final faulty system implementation, which is too expensive to correct. The basic requirements ambiguity resolution approaches in the literature are ambiguity detection, ambiguity avoidance, and ambiguity prevention. Ambiguity prevention is the least tackled approach because it requires designing formal languages and templates, which are hard to implement. The main goal of this paper is to provide full implementation of an ambiguity prevention tool and then study its effectiveness using real requirements. Towards this goal, we developed a set of Finite State Machine (FSMs) implementing templates of various requirement types. We then used Python to implement the ambiguity prevention tool based on those FSMs. We also collected a benchmark of 2460 real requirements and selected a random set of forty real requirements to test the effectiveness of the developed tool. The experiment showed that the implemented ambiguity prevention tool can prevent critical requirements ambiguity issues such as missing information or domain ambiguity. Nevertheless, there is a tradeoff between ambiguity prevention and the effort needed to write the requirements using the imposed templates.

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Semi-automation for ambiguity resolution in Open Source Software requirements

Cited by 11 publications

References 18 publications

Utilizing Creator Profiles for Predicting Valuable User Enhancement Reports

Utilizing Creator Profiles for Predicting Valuable User Enhancement Reports

Building an integrated requirements engineering process based on Intelligent Systems and Semantic Reasoning on the basis of a systematic analysis of existing proposals

Implementation of a Formal Software Requirements Ambiguity Prevention Tool

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