اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدA Syntactic-Semantic Approach to Incremental Verification
110
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Software verification of evolving systems is challenging mainstream methodologies and tools. Formal verification techniques often conflict with the time constraints imposed by change management practices for evolving systems. Since changes in these systems are often local to restricted parts, an incremental verification approach could be beneficial. This paper introduces SiDECAR, a general framework for the definition of verification procedures, which are made incremental by the framework itself. Verification procedures are driven by the syntactic structure (defined by a grammar) of the system and encoded as semantic attributes associated with the grammar. Incrementality is achieved by coupling the evaluation of semantic attributes with an incremental parsing technique. We show the application of SiDECAR to the definition of two verification procedures: probabilistic verification of reliability requirements and verification of safety properties.
قيم البحث
اقرأ أيضاً
Semantic relationships, such as hyponym-hypernym, cause-effect, meronym-holonym etc. between a pair of entities in a sentence are usually reflected through syntactic patterns. Automatic extraction of such patterns benefits several downstream tasks, i
ncluding, entity extraction, ontology building, and question answering. Unfortunately, automatic extraction of such patterns has not yet received much attention from NLP and information retrieval researchers. In this work, we propose an attention-based supervised deep learning model, ASPER, which extracts syntactic patterns between entities exhibiting a given semantic relation in the sentential context. We validate the performance of ASPER on three distinct semantic relations -- hyponym-hypernym, cause-effect, and meronym-holonym on six datasets. Experimental results show that for all these semantic relations, ASPER can automatically identify a collection of syntactic patterns reflecting the existence of such a relation between a pair of entities in a sentence. In comparison to the existing methodologies of syntactic pattern extraction, ASPERs performance is substantially superior.
Machine translation has wide applications in daily life. In mission-critical applications such as translating official documents, incorrect translation can have unpleasant or sometimes catastrophic consequences. This motivates recent research on test
ing methodologies for machine translation systems. Existing methodologies mostly rely on metamorphic relations designed at the textual level (e.g., Levenshtein distance) or syntactic level (e.g., the distance between grammar structures) to determine the correctness of translation results. However, these metamorphic relations do not consider whether the original and translated sentences have the same meaning (i.e., Semantic similarity). Therefore, in this paper, we propose SemMT, an automatic testing approach for machine translation systems based on semantic similarity checking. SemMT applies round-trip translation and measures the semantic similarity between the original and translated sentences. Our insight is that the semantics expressed by the logic and numeric constraint in sentences can be captured using regular expressions (or deterministic finite automata) where efficient equivalence/similarity checking algorithms are available. Leveraging the insight, we propose three semantic similarity metrics and implement them in SemMT. The experiment result reveals SemMT can achieve higher effectiveness compared with state-of-the-art works, achieving an increase of 21% and 23% on accuracy and F-Score, respectively. We also explore potential improvements that can be achieved when proper combinations of metrics are adopted. Finally, we discuss a solution to locate the suspicious trip in round-trip translation, which may shed lights on further exploration.
While numerous attempts have been made to jointly parse syntax and semantics, high performance in one domain typically comes at the price of performance in the other. This trade-off contradicts the large body of research focusing on the rich interact
ions at the syntax-semantics interface. We explore multiple model architectures which allow us to exploit the rich syntactic and semantic annotations contained in the Universal Decompositional Semantics (UDS) dataset, jointly parsing Universal Dependencies and UDS to obtain state-of-the-art results in both formalisms. We analyze the behaviour of a joint model of syntax and semantics, finding patterns supported by linguistic theory at the syntax-semantics interface. We then investigate to what degree joint modeling generalizes to a multilingual setting, where we find similar trends across 8 languages.
During the development and verification of complex airborne systems, a variety of languages and development environments are used for different levels of the system hierarchy. As a result, there may be manual steps to translate requirements between t
hese different environments. This paper presents a tool-supported export technique that translates high-level requirements from the software architecture modeling environment into observers of requirements that can be used for verification in the software component environment. This allows efficient verification that the component designs comply with their high-level requirements. It also provides an automated tool chain supporting formal verification from system requirements down to low-level software requirements that is consistent with certification guidance for avionics systems. The effectiveness of the technique has been evaluated and demonstrated on a medical infusion pump and an aircraft wheel braking system.
In the last few years it has been seen that many software vendors have started delivering projects incrementally with very short release cycles. Best examples of success of this approach has been Ubuntu Operating system that has a 6 months release cy
cle and popular web browsers such as Google Chrome, Opera, Mozilla Firefox. However there is very little knowledge available to the project managers to validate the chosen release cycle length. We propose a decision support system that helps to validate and estimate release cycle length in the early development phase by assuming that release cycle length is directly affected by three factors, (i) choosing right requirements for current cycle, (ii) estimating proximal time for each requirement, (iii) requirement wise feedback from last iteration based on product reception, model accuracy and failed requirements. We have altered and used the EVOLVE technique proposed by G. Ruhe to select best requirements for current cycle and map it to time domain using UCP (Use Case Points) based estimation and feedback factors. The model has been evaluated on both in-house as well as industry projects.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
