Software development is prone to software faults due to the involvement of multiple stakeholders especially during the fuzzy phases (requirements and design). Software inspections are commonly used in industry to detect and fix problems in requirements and design artifacts, thereby mitigating the fault propagation to later phases where the same faults are harder to find and fix. The output of an inspection process is list of faults that are present in software requirements specification document (SRS). The artifact author must manually read through the reviews and differentiate between true-faults and false-positives before fixing the faults. The first goal of this research is to automate the detection of useful vs. non-useful reviews. Next, post-inspection, requirements author has to manually extract key problematic topics from useful reviews that can be mapped to individual requirements in an SRS to identify fault-prone requirements. The second goal of this research is to automate this mapping by employing Key phrase extraction (KPE) algorithms and semantic analysis (SA) approaches to identify fault-prone requirements. During fault-fixations, the author has to manually verify the requirements that could have been impacted by a fix. The third goal of my research is to assist the authors post-inspection to handle change impact analysis (CIA) during fault fixation using NL processing with semantic analysis and mining solutions from graph theory. The selection of quality inspectors during inspections is pertinent to be able to carry out post-inspection tasks accurately. The fourth goal of this research is to identify skilled inspectors using various classification and feature selection approaches. The dissertation has led to the development of automated solution that can identify useful reviews, help identify skilled inspectors, extract most prominent topics/keyphrases from fault logs; and help RE author during the fault-fixation post inspection.

Cette thèse cherche à marier les enseignements de la linguistique et des sciences de la rédaction aux réalités très particulières du texte de loi en français au Canada. L’intelligibilité du texte de loi participe à la fois de l’élaboration du contenu juridique et de sa compréhension par le lecteur qui reçoit le texte fini. La manière dont les phrases sont construites est donc cruciale, puisqu’elles sont le vecteur de l’information à l’échelle de l’article, berceau de la règle de droit. La thèse prend appui à la fois dans les ouvrages portant sur la rédaction législative et dans les ouvrages portant sur la lecture et la compréhension de textes, et cherche à trouver des manières de rendre le texte de loi plus intelligible. Elle explore les assises des recommandations formulées par les experts en légistique et les compare aux connaissances établies en sciences du langage. Elle illustre l’importance de l’agencement logique des « sous-phrases » dans l’atteinte de l’intelligibilité du texte de loi à l’aide d’exemples de dispositions législatives actuelles qui gagneraient à être retravaillées. Le changement de paradigme vers le citoyen-lecteur est essentiel pour qu’il comprenne ses droits, et pour que tous participent à l’amélioration du droit. Cela passe par la recherche de l’intelligibilité plutôt que de la simple lisibilité. Or ce n’est pas le cas actuellement : la phrase dans les textes de loi n’est pas rédigée en suivant des recommandations qui favorisent la compréhension du texte par le lecteur. La phrase législative est réellement distincte de la phrase « ordinaire », or il ne devrait pas y avoir de syntaxe juridique. L’inadéquation entre le langage juridique et le langage ordinaire contribue à alimenter la problématique actuelle de l’accès à la justice au Québec et au Canada. Cette thèse se veut un préambule théorique à de prochaines recherches empiriques sur l’amélioration effective de l’intelligibilité des textes de loi en français.

This paper presents a syntactic analysis of the modal particles in Hausa. The research shows that modal particles may appear at all phrase and sentence borders but that they may not appear in initial position.

Procedural generation algorithms can infer rules based on a dataset of examples when each example is made up of labeled components. Unfortunately, musical sequences resist potential inclusion in these kinds of datasets because they lack explicit structural semantics. In order to algorithmically transform a musical sequence into a sequence of labeled components, a segmentation process is needed. We outline a solution to the challenge of musical phrase segmentation that uses grammatical induction algorithms, a class of algorithms which infer a context-free grammar from an input sequence. We study five different grammatical induction algorithms on three different datasets, one of which is introduced in this work. Additionally, we test how the performance of each algorithm varies when transforming musical sequences using viewpoint combinations. Our experiments show that the algorithm longestFirst achieves the best F1 scores across all three datasets, and that viewpoint combinations which include the duration viewpoint result in the best performance.

The past century has brought with it many changes to the English language. One of these is the drastic increase in complex nominal phrases, particularly premodifiers. This implies difficulties for translators, whose target languages may not have evolved in the same way, and who must then find other solutions. The aim of this essay is to investigate which kind of premodifier is most frequent in an academic text in English, and how the different kinds of premodifiers are translated into Swedish. Since the language pairs share many similarities, it was expected that many of the shorter premodifiers will keep their structure when translated. However, since Swedish cannot recreate the longer and more complex noun phrases, nor add multiple noun premodifiers in succession, it is also expected that several of the English premodifiers will be restructured into other constructions.The results show that the majority of the adjective/participial premodifiers kept their structure when translated into Swedish, and the tendency to restructure them into postmodifiers was low. Noun premodifiers were more likely to be restructured into postmodifiers, especially when they were more complex, though most of the noun premodifiers were restructured into compound nouns. The hyphenated premodifiers were the most likely to be restructured into different constructions, especially postmodifiers.In conclusion, since Swedish and English are similar in structure, many of the premodifiers were quite straightforward in translation, but several, especially longer and more complex noun phrases, can pose problems for a translator.

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What explains the shape of Zauzou noun phrases? Zauzou (Trans-Himalayan, China) noun phrases exhibit considerable diversity in both the choice of the phrase's primary reference type, and the presence of classifiers, demonstratives, modifiers, and case marking. This investigation uses a large, previously existing Zauzou textual corpus. The corpus was annotated for variables hypothesized to predict the variation in noun phrase form. Syntactic variables investigated include word order, subordination, subordinate role, and a new variable called "loneliness." Participant semantic variables include thematic role, agency, and affectedness. Referential semantic variables include boundedness, number, and animacy. The information packaging variable investigated is accessibility. Statistical analysis of the corpus revealed that case marking was predicted using a variable called "loneliness." This is where a multivalent verb has only one argument that is explicitly referenced in the clause. Lonely noun phrases are more likely to be case marked. The role of loneliness in motivating case marking confirms that disambiguation can be an explanation for differential case marking. Animacy and accessibility are important predictors of noun phrase weight. Overall, high animacy and high accessibility correspond to reduced noun phrase weight. Agency and thematic role were also significant variables. The Zauzou data makes clear that speech act participants occupy a unique role in the animacy hierarchy. Speech act participants are often unexpectedly light upon first mention, being referred to with a pronoun or zero anaphor. They are often unexpectedly heavy while highly activated, remaining a pronoun instead of reducing to a zero anaphor. Zauzou, like Mandarin and Cantonese, allows classifiers to be used with a noun but without a numeral. In Mandarin, this construction is used only with new or generic noun phrases. In Cantonese, this construction can be used with noun phrases of any accessibility value. Zauzou occupies a unique intermediate position. In Zauzou, a noun with bare noun phrase can occur with new or old noun phrases, but rarely with active ones. This thesis provides evidence for the importance of text corpora. Using a corpus allowed for the simultaneous inclusion of many variables as well as the consideration of genre effects. In addition, the annotated corpus produced in this investigation is an important output; it is available in the supplemental materials accompanying this thesis.

Indiana University-Purdue University Indianapolis (IUPUI) / With the proliferation of computer science in recent years in modern society, the number of computer science-related employment is expanding quickly. Software engineer has been chosen as the best job for 2023 based on pay, stress level, opportunity for professional growth, and balance between work and personal life. This was decided by a rankings of different news, journals, and publications. Computer science occupations are anticipated to be in high demand not just in 2023, but also for the foreseeable future. It's not surprising that the number of computer science students at universities is growing and will continue to grow. The enormous increase in student enrolment in many subdisciplines of computers has presented some distinct issues. If computer science is to be incorporated into the K-12 curriculum, it is vital that K-12 educators are competent. But one of the biggest problems with this plan is that there aren't enough trained computer science professors. Numerous new fields and applications, for instance, are being introduced to computer science. In addition, it is difficult for schools to recruit skilled computer science instructors for a variety of reasons including low salary issue. Utilizing the K-12 teachers who are already in the schools, have a love for teaching, and consider teaching as a vocation is therefore the most effective strategy to improve or fix this issue. So, if we want teachers to quickly grasp computer science topics, we need to give them an easy way to learn about computer science. To simplify and expedite the study of computer science, we must acquaint school-treachers with the terminology associated with computer science concepts so they can know which things they need to learn according to their profile. If we want to make it easier for schoolteachers to comprehend computer science concepts, it would be ideal if we could provide them with a tree of words and phrases from which they could determine where the phrases originated and which phrases are connected to them so that they can be effectively learned. To find a good concept word or phrase, we must first identify concepts and then establish their connections or linkages. As computer science is a fast developing field, its nomenclature is also expanding at a frenetic rate. Therefore, adding all concepts and terms to the knowledge graph would be a challenging endeavor. Cre- ating a system that automatically adds all computer science domain terms to the knowledge graph would be a straightforward solution to the issue. We have identified knowledge graph use cases for the schoolteacher training program, which motivates the development of a knowledge graph. We have analyzed the knowledge graph's use case and the knowledge graph's ideal characteristics. We have designed a webbased system for adding, editing, and removing words from a knowledge graph. In addition, a term or phrase can be represented with its children list, parent list, and synonym list for enhanced comprehension. We' ve developed an automated system for extracting words and phrases that can extract computer science idea phrases from any supplied text, therefore enriching the knowledge graph. Therefore, we have designed the knowledge graph for use in teacher education so that school-teachers can educate K-12 students computer science topicses effectively.