Using Semantic Analysis and Graph Mining Approaches to Support Software Fault Fixation

Software requirements analysis is crucial for any software project and it is the basis of requirements reuse within Software Product Line engineering. Software requirements specifications are usually expressed in natural language, which are informal, imprecise and ambiguous, thus analyzing them automatically is a challenging task. Although methods towards automatic analysis of software requirements have been studied before, many of them have limitations and effective researches in this area are still lacking. Therefore, in this paper a new approach was proposed to automatically extract structured information of functional requirements from Software Requirements Specifications in natural language. The methods of machine learning, natural language processing and semantic analysis were employed and combined in this approach. With a 10-fold cross validation setting, the method was evaluated on a manually annotated corpus. The experiments show that this approach achieves a good performance. Moreover, it was found that the model trained on the requirements dataset of the E-commerce systems, can be used to extract semantic information from the requirements of auto-maker systems. The cross-domain extraction results show that the method of this paper is domain independent and robust to some extent.

Software development starts by specifying the requirements. A Software Requirements Specification (SRS) describes what the software must do. Naturally, the SRS takes the core role as the descriptive documentation at every phase of the development cycle. To avoid problems in the latter development phases and reduce life-cycle costs, it is crucial to ensure that the specification is correct. This paper describes how to model, test and evaluate (i.e., check, examine, and probe) a natural language (NL) SRS using two formalisms (Z and Statecharts). These formalisms are used to determine strategies for avoiding design defects that stem from the requirements that could ultimately lead to system failures. A case study was performed to validate the integrity of a Guidance Control SRS in terms of completeness, consistency, and fault-tolerance. Based on these experiences, the NL-specification→Z→Statechart transformations can be completed in a systematic and repeatable manner that yield valuable insight into the overall integrity of software specifications.

Android access control granularity based on its permission mechanism is relatively coarse, which cannot effectively protect the user privacy. Many Android applications do not strictly abide by the principle of least privilege (PLP). Both benign and malicious apps may request more permissions than those they really use. We rethink previous permission over-claim problem of Android applications, and extend it to three kinds of problems: Explicit Permission Over-claim, Implicit Permission Over-claim and Ad Library Permission Over-claim. The latter two problems are new that have not been raised by any previous work. Static analysis is to decompile the applications to generate intermediate code and then analyze the usage of permissions. Our static analysis on 10710 applications shows that 76.08% of them may have Explicit Permission Over-claim problem, among those there are 424 applications that have sensitive permissions, which are only used in the advertisement library’s code of the applications rather than developer’s own code. They have Ad Library Permission Over-claim problem. The main idea of our semantic analysis is to calculate the semantic similarity between apps’ descriptions and function phrases. If the similarity exceeds a certain threshold, the app is considered relevant to the corresponding function. We compare the results of the semantic analysis with those of manual reading of 102 Android application descriptions. The F-measures of the three chosen functions are 80.82%, 70.48% and 89.62%, respectively. The evaluation results show our method can efficiently detect the above three kinds of permission over claim problems which indicates that our method would be helpful for normal users to have a clear understanding of permission usage of Android applications.

The paper describes application of the semantic rule-based sentiment analysis approach, which was earlier developed and tested on English texts, to the Russian language. In order to take into account specificity of Russian it was adapted, particularly representation of the rules as patterns over a list of words was replaced with algorithms over the syntax tree of a sentence. The experiments on a quarter of a corpus of sentences extracted from hotel reviews allowed to perform the error analysis and refinement of the approach. The final results on the whole corpus allowed to achieve the results close to the state-of-the-art methods based on neural networks. The advantages of the approach, including simple interpretability of its results and absence of the need of learning, make it perspective for further research in sentiment analysis.

Natural language (NL) is pervasive in software requirements specifications (SRSs). However, despite its popularity and widespread use, NL is highly prone to quality issues such as vagueness, ambiguity, and incompleteness. Controlled natural languages (CNLs) have been proposed as a way to prevent quality problems in requirements documents, while maintaining the flexibility to write and communicate requirements in an intuitive and universally understood manner. In collaboration with an industrial partner from the financial domain, we systematically develop and evaluate a CNL, named Rimay, intended at helping analysts write functional requirements. We rely on Grounded Theory for building Rimay and follow well-known guidelines for conducting and reporting industrial case study research. Our main contributions are: (1) a qualitative methodology to systematically define a CNL for functional requirements; this methodology is intended to be general for use across information-system domains, (2) a CNL grammar to represent functional requirements; this grammar is derived from our experience in the financial domain, but should be applicable, possibly with adaptations, to other information-system domains, and (3) an empirical evaluation of our CNL (Rimay) through an industrial case study. Our contributions draw on 15 representative SRSs, collectively containing 3215 NL requirements statements from the financial domain. Our evaluation shows that Rimay is expressive enough to capture, on average, 88% (405 out of 460) of the NL requirements statements in four previously unseen SRSs from the financial domain.

This paper addresses the problem of the in software requirement specification (SRS) expressed in natural language. Due to rapid changes in technology and business frequently witnessed over time, the original SRS documents often experience the problems of missing, not available, and hard-to-locate requirements. One of the flaws in earlier solutions to this problem has no consideration for missing requirements from multiple viewpoints. Furthermore, since such SRS documents represent an incomplete domain model, manual discovery (identification and incorporation) of missing requirements is highly labor intensive and error-prone. Consequently, deriving and improving an efficient adaptation of SRS changes remain a complex problem. In this paper, we present a new methodology entitled Proxy Viewpoints Model-based Requirements Discovery (PVRD). Through the requirements discovery and analysis process, PVRD methodology provides ways to construct proxy viewpoints model from legacy status requirements. Requirements term expansion technique facilitates the retrieval process of requirements of interest based on the improved requirements representation space in proxy viewpoints model. The PVRD methodology provides an integrated environment that supports requirements discovery process as well as efficient management.

Software Development Life Cycle (SDLC) starts with eliciting requirement of user as a document called Software Requirement Specification (SRS). SRS document is mostly written in the form of any natural language (NL) that is convenient for the client. In order to develop a right software based on user's requirements, the objects, methods and attributes needs to be identified from SRS document. In this paper, an attempt is made to develop a methodology, using the concept of Natural Language Processing (NLP) for Object Oriented (OO) Programming System analysis concept, by finding out the class name and its details directly form SRS.

An accurate forecast of the final manufacturing costs in the early product development phase is a key element, especially in mechanical engineering. An accurate and reliable prediction allows, on the one hand, estimating the economic feasibility and, on the other hand, deriving necessary cost reductions. It is essential to evaluate and monitor compliance with the cost target initially and throughout the entire development process. Most cost estimation methods are primarily based on simple heuristic and statistical approaches that provide only limited accurate predictions in the early development phase. Sufficiently precise cost estimations, especially for customer-specific developments, are only possible in the late phase of the development process using finely parameterized models and cost rates at the component level. The supporting use of Machine Learning (ML) has not yet played a major role in cost estimation in mechanical engineering. Therefore, this article shows the general applicability of ML for predicting manufacturing costs in mechanical engineering and that even in the early phase of product development, precise predictions with little known information about the final product are possible. In addition, we present a generic feature set for ML-based cost prediction regarding machinery. Furthermore, the quality of different created ML models was evaluated based on real data of a mechanical engineering use case, and high accuracy in predicting manufacturing costs was demonstrated

The traditional approach to automated analysis of medical data is mostly based on statistical or theoretical-decision methods of pattern recognition. Using such methods, we can obtain many valuable items, e.g., tracking patients' movements, control of medical treatment, etc. In medical informatics there are still many problems not solvable by computers and reserved for human medical staff. Such problems can be solved by the development of scientific research toward machine intelligence. In this paper, we try to show how the computer can understand medical data instead of simple processing and analysis. Sometimes it may be useful to make semantic content analysis leading to automatic understanding of medical data, e.g., for intelligent help in the diagnosis process or in semantics-based searching through medical databases. This paper will present the application of the cognitive-based approach for intelligent semantic analysis, allowing describing automatically important diagnostic features of analyzed images. This approach is based on a special kind of image description language and grammar formalism. During the linguistic analysis of medical patterns, we can solve the problem of generalization of features of a selected image and obtaining semantic content description of the image. The most important part of this analysis depends on the cognitive resonance process, in which features of real images are compared with some kind of expectation taken from the knowledge base containing knowledge regarding pathological cases originating from medical practice.

Organizations provide a platform for the users to express their opinion on the product in the form of reviews. Spam reviews are irrelevant reviews that mislead the consumers. In this paper we discuss semantic and machine learning approaches to classify reviews of trending products and provide rating to the reviews. We have used semantic and machine learning algorithms on five different products’ dataset. We have collected datasets comprising of both spam and non-spam reviews for training and testing purposes. We have obtained an average accuracy of 82.2% for the classification and 84.4% for review rating considering all the five products using Semantic approach. Similarly, we have obtained an accuracy of 82.2% using machine learning for the classification. For rating the review, we have obtained accuracy of 89.4% using machine learning. We found that both semantic and machine learning approaches perform well for classification of reviews. However for rating of reviews we found machine learning approach performed marginally better than semantic approach.

Requirements Engineering (RE) is concerned with the gathering, analyzing, specifying and validating of user requirements that are documented mostly in natural language. The artifact produced by the RE process is the software requirements specification (SRS) document. The success of a software project largely depends on the quality of SRS documentation, which serves as an input to the design, coding and testing phases. This paper approaches the problem of the automatic quality assessment of textual requirements from an innovative point of view, namely the use of the Natural Language Processing (NLP) text classification technique. The paper proposes a quality model for the requirements text and a text classification system to automate the quality assessment process. A large study evaluating the discriminatory power of the quality characteristics and the feasibility of a tool for the automatic detection of ambiguities in requirements documentation is presented. The study also provides a benchmark for such an evaluation and an upper bound on what we can expect automatic requirements quality assessment tools to achieve. The reported research is part of a larger project on the applicability of NLP techniques to assess the quality of artifacts produced in RE.

We propose a software requirements analysis method based on domain ontology technique, where we can establish a mapping between a software requirements specification and the domain ontology that represents semantic components. Our ontology system consists of a thesaurus and inference rules and the thesaurus part comprises domain specific concepts and relationships suitable for semantic processing. It allows requirements engineers to analyze a requirements specification with respect to the semantics of the application domain. More concretely, we demonstrate following three kinds of semantic processing through a case study, (1) detecting incompleteness and inconsistency included in a requirements specification, (2) measuring the quality of a specification with respect to its meaning and (3) predicting requirements changes based on semantic analysis on a change history.

An inconsistent and ambiguous Software Requirement Specification (SRS) document results in an erroneous/failed software project. Hence, it is a serious challenge to handle and process complex and ambiguous requirements. Most of the literature work focuses on detection and resolution of ambiguity in software requirements. Also, there is no standardized way to write unambiguous and consistent requirements. The goal of this research was to generate an ambiguity-less SRS document. This paper presents a new approach to write ambiguity-less requirements. Furthermore, we design a framework for Natural Language (NL) to Controlled Natural Language (CNL) (such as Semantic Business Vocabulary and Rules (SBVR)) transition and develop a prototype. The prototype also generates Resource Description Framework (RDF) representation. The SBVR has a shared meaning concept that minimizes ambiguity, and RDF representation is supported by query language such as SPARQL Protocol and RDF Query Language (SPARQL). The proposed approach can help software engineers to translate NL requirements into a format that is understandable by all stakeholders and also is machine processable. The results of our prototype are encouraging, exhibiting the efficient performance of our developed prototype in terms of usability and correctness.

One of the most critical steps to integrating heterogeneous e-Business applications using different XML schemas is schema mapping, which is known to be costly and error-prone. Past research on schema mapping has not fully utilized semantic information in the XML schemas. In this paper, we propose a semantic similarity analysis approach to facilitate XML schema mapping, merging and reuse. Several key innovations are introduced to better utilize available semantic information. These innovations, including: 1) a layered semantic structure of XML schema, 2) layered specific similarity measures using an information content based approach, and 3) a scheme for integrating similarities at all layers. Experimental results using two different schemas from an real world application demonstrate that the proposed approach is valuable for addressing difficulties in XML schema mapping.

Delays are among the most crucial adversaries to the success and performance of construction projects, making delay analysis and management a critical task for project managers. This task will be highly complicated in large-scale projects such as construction, which usually consist of a complex network of heterogeneous entities in continuous interaction. Traditional approaches and methods for the analysis of delays and their causes have been criticised for their ability to handle complex projects, and for considering the interrelationships between delay causes. Addressing this gap, this research introduces an alternative approach for delay causes analysis by adopting Semantic Network Analysis (SNA) method. The paper reports the results from an investigation of delays in construction projects in the Oil-Gas-Petrochemical sector using SNA. The method’s capacity to identify and rank delay causes, which can assist managers in selecting appropriate measures for eliminating them, are empirically examined and discussed. The paper argues that SNA leads to a more comprehensive understanding of the main causes of delay in large and complex projects, allowing a better identification and mapping of the interrelationships between these discrete factors.