Goal-conflict likelihood assessment based on model counting

Degiovanni, Renzo; Castro, Pablo F.; Arroyo, Marcelo; Ruiz, Marcelo; Aguirre, Nazareno; Frias, Marcelo F.

doi:10.1145/3180155.3180261

Cited by 15 publications

(38 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These circumstances are redundant, so p'3 = ♦ ((h A -m A p) A 0 ( h A p A m)) stands for the circumstances captured by p 3. We find that p i is more likely than p'3 using the assessment method [8], so p i should be prioritized. Situations like this show that the accuracy of the assessment method based on likelihood is sensitive to redundant circumstances.…”

Section: = ♦ ((H a -mentioning

confidence: 85%

“…In the assessment stage, in order to give engineers more guidance on which BCs need to get attention, probabilities [3] of their occurrence are considered as an important indicator. For systems without extra probabilistic information, there is an approach [8] based on model counting to analyze the likelihood of BCs. It is defined as follows.…”

Section: A Goal-conflict Analysismentioning

confidence: 99%

“…However, the generality metric cannot evaluate p i and p 3 since the generality relationship between them does not hold. In the assessment stage, if we compute the likelihood based on the method [8], we can classify p 3 as being more likely than p i in the long term, and engineers should prioritize p 3 in the search for mechanisms that would allow us to reduce the chances of reaching p 3.…”

Section: = ♦ ((H a -mentioning

confidence: 99%

“…Unfortunately, the assessment method [8] lacks the accuracy to compute the likelihood by model counting because some models are meaningless, i.e., there does not exist the circumstances to lead the divergence in reality. Considering the circumstances captured by p 3, we observe that the circumstances (1), ( 3), (4), and (5) violate gi .…”

Section: = ♦ ((H a -mentioning

confidence: 99%

“…The assessment stage is concerned with evaluating how likely the identified conflicts are, and how likely and severe are their consequences. Degiovanni et al [8] proposed an automatically assessing method based on model counting, which can be used to prioritize BCs to be resolved. However, a set of general BCs misleads to prioritize the BCs because a general BC potentially captures redundant circumstances that do not lead to a divergence.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

How to Identify Boundary Conditions with Contrasty Metric?

Luo

Wan

Song

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

View full text Add to dashboard Cite

The boundary conditions (BCs) have shown great potential in requirements engineering because a BC captures the particular combination of circumstances, i.e., divergence, in which the goals of the requirement cannot be satisfied as a whole. Existing researches have attempted to automatically identify lots of BCs. Unfortunately, a large number of identified BCs make assessing and resolving divergences expensive. Existing methods adopt a coarse-grained metric, generality, to filter out less general BCs. However, the results still retain a large number of redundant BCs since a general BC potentially captures redundant circumstances that do not lead to a divergence. Furthermore, the likelihood of BC can be misled by redundant BCs resulting in costly repeatedly assessing and resolving divergences.In this paper, we present a fine-grained metric to filter out the redundant BCs. We first introduce the concept of contrasty of BC. Intuitively, if two BCs are contrastive, they capture different divergences. We argue that a set of contrastive BCs should be recommended to engineers, rather than a set of general BCs that potentially only indicates the same divergence. Then we design a post-processing framework (P P F c ) to produce a set of contrastive BCs after identifying BCs. Experimental results show that the contrasty metric dramatically reduces the number of BCs recommended to engineers. Results also demonstrate that lots of BCs identified by the state-of-the-art method are redundant in most cases. Besides, to improve efficiency, we propose a joint framework (J F c ) to interleave assessing based on the contrasty metric with identifying BCs. The primary intuition behind J F c is that it considers the search bias toward contrastive BCs during

show abstract

Section: = ♦ ((H a -mentioning

confidence: 85%

Section: A Goal-conflict Analysismentioning

confidence: 99%

Section: = ♦ ((H a -mentioning

confidence: 99%

Section: = ♦ ((H a -mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

How to Identify Boundary Conditions with Contrasty Metric?

Luo

Wan

Song

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

View full text Add to dashboard Cite

show abstract

ACoRe: Automated Goal-Conflict Resolution

Carvalho

Degiovanni

Brizzio

et al. 2023

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

System goals are the statements that, in the context of software requirements specification, capture how the software should behave. Many times, the understanding of stakeholders on what the system should do, as captured in the goals, can lead to different problems, from clearly contradicting goals, to more subtle situations in which the satisfaction of some goals inhibits the satisfaction of others. These latter issues, called goal divergences, are the subject of goal conflict analysis, which consists of identifying, assessing, and resolving divergences, as part of a more general activity known as goal refinement.While there exist techniques that, when requirements are expressed formally, can automatically identify and assess goal conflicts, there is currently no automated approach to support engineers in resolving identified divergences. In this paper, we present ACoRe, the first approach that automatically proposes potential resolutions to goal conflicts, in requirements specifications formally captured using linear-time temporal logic. ACoRe systematically explores syntactic modifications of the conflicting specifications, aiming at obtaining resolutions that disable previously identified conflicts, while preserving specification consistency. ACoRe integrates modern multi-objective search algorithms (in particular, NSGA-III, WBGA, and AMOSA) to produce resolutions that maintain coherence with the original conflicting specification, by searching for specifications that are either syntactically or semantically similar to the original specification.We assess ACoRe on 25 requirements specifications taken from the literature. We show that ACoRe can successfully produce various conflict resolutions for each of the analyzed case studies, including resolutions that resemble specification repairs manually provided as part of conflict analyses.

show abstract

Efficient Reactive Synthesis Using Mode Decomposition

Brizzio,

Sánchez

2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Goal-conflict likelihood assessment based on model counting

Cited by 15 publications

References 37 publications

How to Identify Boundary Conditions with Contrasty Metric?

How to Identify Boundary Conditions with Contrasty Metric?

ACoRe: Automated Goal-Conflict Resolution

Efficient Reactive Synthesis Using Mode Decomposition

Contact Info

Product

Resources

About