In the past few years, web documents, blogs, and reviews have played an important role in many fields as organizations always aim to find consumer or public opinions about their products and services. On the other hand, individual consumers also seek the opinions or emotions of existing users of a certain product before purchasing it. This method is currently one of the most vital methods for adapting the organizations' plans. In this article, the authors provide a survey for different techniques and approaches for emotion analysis from the text. They also demonstrate the techniques and the methods that have been proposed by different researchers with criticizing many of these methods according to the applied approach and the accuracy level.

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Over the recent past, the focus of EDA tools has been on tackling the chip issues. Today, system design issues are emerging as the top concerns of design teams. The issues include the inability to simulate at the system level, incomplete and inaccurate system specs, and immature hardware/software co-design. Co-design is an essential element of new design methodologies for system development:• ratios of seven software engineers per hardware engineer are now common in the development of complex embedded systems; • errors cost 10 times more to correct at the integration stage than during design; • system specifications change impacting both hardware and software development; • interfaces between hardware and software are non-trivial for system design. A comprehensive hardware/software co-design methodology needs to deliver a unified implementation environment addressing the issues outlined above. What kind of tools can meet this challenge? Are EDA vendors close to providing the tools that the designers need? Brian Bailey, Mentor Graphics, Wilsonville, OROver the past few years a number of commercial products have been introduced that address various parts of the system verification problem. The recent introductions have focused on the hardware/software boundary. These tools are a start, but the field is very much in its infancy. Today's tools are limited to a single abstraction of software and in most cases a single hardware simulation session. This makes the tools useful as an implementation verification solution and enables the notion of a virtual hardware prototype upon which we can perform software verification. It does not make them useful for higher level design or concept validation. This requires tools that are much more flexible and can cross many abstraction domains. At the same time, these hardware/software simulators must join with other parts of the system verification problem to create a complete verification solution. As with all simulation solutions, models are a key issue, both in terms of availability and accuracy. It appears as if many core providers are stepping up to the challenge of providing these simulation models for processors or are willing to assist in the validation of models. This will be a key step in the wide-spread acceptance of these tools. Kurt Keutzer, Synopsys, Mountain View, CAThere is no single hardware-software co-simulation problem and as a result there is no single tool or technology that can successfully solve all the problems associated with co-verification of hardware and software. Each developer must trade off the desired performance against the level of accuracy. Accuracy itself has two aspects. The first is the timing accuracy. The second might be called veracity, or the extent to which the model accurately models the design under verification. Once these parameters are fixed then the remaining challenge is to determine which solution comes closest to meeting the requirements at a cost, in time and money, that one is willing to pay. For hardware designers the trade-off...

Software bug localization is an important step in the software maintenance process. Automatic bug localization can reduce the time consumed in the process of localization. Some techniques are applied in the bug localization process, but those techniques suffer from limitations in time and accuracy. This paper proposes a phase-based bug localization approach to overcome these limitations. The approach is composed of three main phases which are raw data preparation, package classification, and source code recommendation. The main input to our approach is a bug report and the source code of the past versions for the target system of interest. From the bug report, various information is utilized: the summary, the description, the stack traces, and the fixed source code files. The raw data preparation phase is used to restructure those inputs. The package classification phase aims to locate the package that would include the source code to be modified as a first step, hence reducing the time needed to locate the source code file due to the lexical mismatch between those files and the bug report data. Bidirectional Encoder Representations from Transformers (BERT), which is a sentence embedding technique, is utilized in the package classification and source code recommendation phases. The experimental results show that our approach outperforms existing approaches according to TOP-N rank and Mean Reciprocal Rank (MRR) evaluation metrics.

Software bug localization is an essential step within the software maintenance activity, consuming about 70% of the time and cost of the software development life cycle. Therefore, the need to enhance the automation process of software bug localization is important. This paper surveys various software bug localization techniques. Furthermore, a running motivational example is utilized throughout the paper. Such motivational example illustrates the surveyed bug localization techniques, while highlighting their pros and cons. The motivational example utilizes different software artifacts that get created throughout the software development lifecycle, and sheds light on those software artifacts that remain poorly utilized within existing bug localization techniques, regardless of the rich wealth of knowledge embedded within them. This research thus presents guidance on what artifacts should future bug localization techniques focus, to enhance the accuracy of bug localization, and speedup the software maintenance process.

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In the past few years, web documents, blogs, and reviews have played an important role in many fields as organizations always aim to find consumer or public opinions about their products and services. On the other hand, individual consumers also seek the opinions or emotions of existing users of a certain product before purchasing it. This method is currently one of the most vital methods for adapting the organizations' plans. In this article, the authors provide a survey for different techniques and approaches for emotion analysis from the text. They also demonstrate the techniques and the methods that have been proposed by different researchers with criticizing many of these methods according to the applied approach and the accuracy level.

Every successful business aims to know how customers feel about its brands, services, and products. People freely express their views, ideas, sentiments, and opinions on social media for their day-to-day activities, for product reviews, for surveys, and even for their public opinions. This process provides a fortune of valuable resources about the market for any type of business. Unfortunately, it's impossible to manually analyze this massive quantity of information. Sentiment analysis (SA) and opinion mining (OM), as new fields of natural language processing, have the potential benefit of analyzing such a huge amount of data. SA or OM is the computational treatment of opinions, sentiments, and subjectivity of text. This chapter introduces the reader to a survey of different text SA and OM proposed techniques and approaches. The authors discuss in detail various approaches to perform a computational treatment for sentiments and opinions with their strengths and drawbacks.