Automating requirements analysis and test case generation

Moitra, Abha; Siu, Kit; Crapo, Andrew; Durling, Michael; Li, Meng; Manolios, Panagiotis; Meiners, Michael A.; McMillan, Craig

doi:10.1007/s00766-019-00316-x

Cited by 15 publications

(12 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Textual representations can be understood easily even by non-experts [6] in formal methods. However, only 17 of the primary studies (15% of all primary studies) use such a representation to provide statements for error notification or highlighting the error in a provided textual input system such as Controlled/-Constrained Natural Language (CNL) [51,52], Structured English [53][54][55][56][57], or error localization as known from programming languages [58][59][60][61][62].…”

Section: Textual Representationmentioning

confidence: 99%

“…Error localization is used to highlight or to identify the cause of an error in a given design or verification model. For example, Pakonen et al [39] highlight atomic proposition values, Beer et al [104] mark errors in a given specification, and ASSERT [56,57] highlights the responsible requirements with error markers. These approaches highlight errors in the given input design model, while the approach by Berg et al [6] renders an explicit explanation, further aiming to ease error comprehension.…”

Section: Answer To Rq3mentioning

confidence: 99%

“…In total 28 primary studies (29%) evaluate their approach in a safety-critical application domain such as automotive (10), robotics (6), avionics (5), nuclear (4), and railway (3). Evaluation of textual representations is performed, e. g., in avionics [55][56][57]107], the railway domain [51], and robotics [53]. Evaluation of graphical representations is performed, e. g., in the automotive [46,109], and nuclear domain [37][38][39]147].…”

Section: Application Domainsmentioning

confidence: 99%

“…Particularly, domain ontologies and vocabularies are used to enhance the counterexample with additional information to ease error comprehension. Crapo et al [55,107] and Moitra et al [56,57] use Requirements Analysis Engine (RAE) and Analysis of Semantic Specifications and Efficient generation of Requirements-based Tests (ASSERT) that accept a formal requirement in an easily understandable syntax by making use of a domain ontology. Further, they analyze an incomplete set of requirements and localizes the error by identifying the responsible requirements with an error marker.…”

Section: Information Enriching Textual Representations Of Counterexam...mentioning

confidence: 99%

See 3 more Smart Citations

A systematic literature review on counterexample explanation

Kaleeswaran,

Nordmann,

Vogel

et al. 2022

Preprint

View full text Add to dashboard Cite

Context: Safety is of paramount importance for cyber-physical systems in domains such as automotive, robotics, and avionics. Formal methods such as model checking are one way to ensure the safety of cyber-physical systems. However, adoption of formal methods in industry is hindered by usability issues, particularly the difficulty of understanding model checking results. Objective: We want to provide an overview of the state of the art for counterexample explanation by investigating the contexts, techniques, and evaluation of research approaches in this field. This overview shall provide an understanding of current and guide future research. Method: To provide this overview, we conducted a systematic literature review. The survey comprises 116 publications that address counterexample explanations for model checking. Results: Most primary studies provide counterexample explanations graphically or as traces, minimize counterexamples to reduce complexity, localize errors in the models expressed in the input formats of model checkers, support linear temporal logic or computation tree logic specifications, and use model checkers of the Smybolic Model Verifier family. Several studies evaluate their approaches in safety-critical domains with industrial applications. Conclusion: We notably see a lack of research on counterexample explanation that targets probabilistic and real-time systems, leverages the explanations to domain-specific models, and evaluates approaches in user studies. We conclude by discussing the adequacy of different types of explanations for users with varying domain and formal methods expertise, showing the need to support laypersons in understanding model checking results to increase adoption of formal methods in industry.

show abstract

Section: Textual Representationmentioning

confidence: 99%

Section: Answer To Rq3mentioning

confidence: 99%

Section: Application Domainsmentioning

confidence: 99%

Section: Information Enriching Textual Representations Of Counterexam...mentioning

confidence: 99%

See 2 more Smart Citations

A systematic literature review on counterexample explanation

Kaleeswaran,

Nordmann,

Vogel

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Another is applying NLP to use cases, in order to create simple sequence diagrams with messages between objects (Segundo et al, 2007), or to assist in initial design (Drazan & Mencl, 2007). A tool has been developed for capturing requirements in controlled natural language, backed by a formal requirements analysis engine and to automatically generate a complete set of requirements-based test cases (Moitra et al, 2019). Another tool has 14th IADIS International Conference Information Systems 2021 introduced the generation of core requirements from product requirements written in a natural language (Reinhartz-Berger & Kemelman, 2020).…”

Section: Introductionmentioning

confidence: 99%

Behavioral Programming With a Subset of Natural Language: An Evaluation Study

2021

14th IADIS International Conference Information Systems

View full text Add to dashboard Cite

We consider the idea of behavioral programming by writing controlled natural language requirements, which are then compiled directly into executable code. Our motivation is to bridge the gap between system requirements and a final system. We claim that formal structured natural language requirements can serve as the means and the end to programming the behavior of reactive systems, if one utilizes a fully executable language that supports story-like sentences, such as live sequence charts (LSC). We use natural language processing methods combined with user interaction in order to understand English requirements, and translate them into LSCs, disambiguating and clarifying intentions with the user when necessary. We test a multi-modal user interface we term show & tell, where natural language is interspersed with play-in, and model-based co-reference resolution. Our approach is domain general and builds the underlying model of the system leading to direct execution. The paper presents novel evaluation studies that show its potential as an effective tool for natural language programming of reactive system behavior.

show abstract

DRIP: Segmenting individual requirements from software requirement documents

Zhao,

Zhang,

Lian

et al. 2023

Softw Pract Exp

View full text Add to dashboard Cite

Numerous academic research projects and industrial tasks related to software engineering require individual requirements as input. Unfortunately, according to our observation, several requirements may be packed in one paragraph without explicit boundaries in specification documents. To understand this problem's prevalence, we performed a preliminary study on the open requirement documents widely used in the academic community over the last 10 years, and found that 26% of them include this phenomenon. Several text segmentation approaches have been reported; however, they tend to identify topically coherent units which may contain more than one requirement. What is more, they do not take the constitutions of semantic units of requirements into consideration. Here we report a two‐phase learning‐based approach named DRIP to segment individual requirements from paragraphs. To be specific, we first propose a Requirement Segmentation Siamese framework, which models the similarity of sentences and their conjunction relations, and then detects the initial boundaries between individual requirements. Then, we optimize the boundaries heuristically based on the semantic completeness validation of the segments. Experiments with 1132 paragraphs and 6826 sentences show that DRIP outperforms the popular unsupervised and supervised text segmentation algorithms with respect to processing different documents (with accuracy gains of 57.65%–187.53%) and processing paragraphs of different complexity (with average accuracy gains of 54.46%–158.68%). We also show the importance of each component of DRIP to the segmentation.

show abstract

Automating requirements analysis and test case generation

Cited by 15 publications

References 30 publications

A systematic literature review on counterexample explanation

A systematic literature review on counterexample explanation

Behavioral Programming With a Subset of Natural Language: An Evaluation Study

DRIP: Segmenting individual requirements from software requirement documents

Contact Info

Product

Resources

About