What Did You Mean? - Facing the Challenges of User-generated Software Requirements

Geierhos, Michaela; Schulze, Sabine; Bäumer, Frederik Simon

doi:10.5220/0005346002770283

Cited by 10 publications

(7 citation statements)

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“…for lexical ambiguity) are or are not given in a requirements description. The accuracy of the indicators could be determined by how many requirements descriptions were correctly classified in relation to all descriptions: a = correct classified descriptions all checked descriptions (1) Conversely, this means that the error rate f can be described by f = 1 − a. However, this measure of the accuracy or error rate is to be regarded as superficial, since there are four possible result combinations (true positive, true negative, false positive, false negative), of Page 5752 which up to now only two are considered (true positives and negatives).…”

Section: Resultsmentioning

confidence: 99%

“…For this reason, a trigger is necessary, that considers both contextual and semantic information. (1) Action Object Refinement I want to send emails with large files…”

Section: Referential Ambiguity Triggermentioning

confidence: 99%

“…functional and non-functional requirements) of the prospective end users. Current techniques for the requirements analysis step often use high-level language to precisely document user requirements, but NL makes it easier for (non-expert) stakeholders to participate in the requirements analysis process [1,2]. Nevertheless, NL requirements descriptions may be considered as challenging because they are often inconsistent, ambiguous and incomplete [3,4,5,6].…”

Section: Introductionmentioning

confidence: 99%

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Flexible Ambiguity Resolution and Incompleteness Detection in Requirements Descriptions via an Indicator-Based Configuration of Text Analysis Pipelines

Bäumer¹,

Geierhos²

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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Naturallanguage software requirements descriptions enable end users to formulate their wishes and expectations for a future software product without much prior knowledge in requirements engineering. However, these descriptions are susceptible to linguistic inaccuracies such as ambiguities and incompleteness that can harm the development process. There is a number of software solutions that can detect deficits in requirements descriptions and partially solve them, but they are often hard to use and not suitable for end users. For this reason, we develop a software system that helps end-users to create unambiguous and complete requirements descriptions by combining existing expert tools and controlling them using automatic compensation strategies.In order to recognize the necessity of individual compensation methods in the descriptions, we have developed linguistic indicators, which we present in this paper. Based on these indicators, the whole text analysis pipeline is ad-hoc configured and thus adapted to the individual circumstances of a requirements description.

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Section: Resultsmentioning

confidence: 99%

“…For this reason, a trigger is necessary, that considers both contextual and semantic information. (1) Action Object Refinement I want to send emails with large files…”

Section: Referential Ambiguity Triggermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Flexible Ambiguity Resolution and Incompleteness Detection in Requirements Descriptions via an Indicator-Based Configuration of Text Analysis Pipelines

Bäumer¹,

Geierhos²

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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“…Here, the Software Specification Language is an example for a language that aims at a comprehensive specification of services while being applicable to non-functional and functional software requirements [4]. Nevertheless, formal specification languages, even in a weaker form, are not applicable for end users [5] because they are neither experts nor have the corresponding knowledge [6]. After all, formal and semi-formal approaches bring invincible issues with them, which force system operators to tolerate freely formulated NL requirement descriptions by end users.…”

Section: Service Descriptions For Otf Computing: Open Challengesmentioning

confidence: 99%

Natural Language Processing in OTF Computing: Challenges and the Need for Interactive Approaches

2019

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The vision of On-the-Fly (OTF) Computing is to compose and provide software services ad hoc, based on requirement descriptions in natural language. Since non-technical users write their software requirements themselves and in unrestricted natural language, deficits occur such as inaccuracy and incompleteness. These deficits are usually met by natural language processing methods, which have to face special challenges in OTF Computing because maximum automation is the goal. In this paper, we present current automatic approaches for solving inaccuracies and incompletenesses in natural language requirement descriptions and elaborate open challenges. In particular, we will discuss the necessity of domain-specific resources and show why, despite far-reaching automation, an intelligent and guided integration of end users into the compensation process is required. In this context, we present our idea of a chat bot that integrates users into the compensation process depending on the given circumstances.

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“…Since there was no assistant system to check the quality for many years (Hussain et al, 2007) we plan to extend the provided system in order to provide some quality analysis of the extracted information. We have already developed concepts to generate suggestions for non-experts, how to complete or clarify their requirement descriptions (Geierhos et al, 2015). Based on these insights, we want to implement a system for the resolution of vagueness and incompleteness of NL requirements.…”

Section: Class Precisionmentioning

confidence: 99%

On- and Off-Topic Classification and Semantic Annotation of User-Generated Software Requirements

Dollmann

Geierhos

2016

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Users prefer natural language software requirements because of their usability and accessibility. When they describe their wishes for software development, they often provide off-topic information. We therefore present REaCT 1 , an automated approach for identifying and semantically annotating the on-topic parts of requirement descriptions. It is designed to support requirement engineers in the elicitation process on detecting and analyzing requirements in user-generated content. Since no lexical resources with domain-specific information about requirements are available, we created a corpus of requirements written in controlled language by instructed users and uncontrolled language by uninstructed users. We annotated these requirements regarding predicate-argument structures, conditions, priorities, motivations and semantic roles and used this information to train classifiers for information extraction purposes. REaCT achieves an accuracy of 92% for the on-and off-topic classification task and an F 1 -measure of 72% for the semantic annotation.

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What Did You Mean? - Facing the Challenges of User-generated Software Requirements

Cited by 10 publications

References 0 publications

Flexible Ambiguity Resolution and Incompleteness Detection in Requirements Descriptions via an Indicator-Based Configuration of Text Analysis Pipelines

Flexible Ambiguity Resolution and Incompleteness Detection in Requirements Descriptions via an Indicator-Based Configuration of Text Analysis Pipelines

Natural Language Processing in OTF Computing: Challenges and the Need for Interactive Approaches

On- and Off-Topic Classification and Semantic Annotation of User-Generated Software Requirements

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