Abstract Bad requirements quality can cause expensive consequences during the software development lifecycle, especially if iterations are long and feedback comes late. We aim at a light-weight static requirements analysis approach that allows for rapid checks immediately when requirements are written down. We transfer the concept of code smells to requirements engineering as Requirements Smells . To evaluate the benefits and limitations, we define Requirements Smells, realize our concepts for a smell detection in a prototype called Smella and apply Smella in a series of cases provided by three industrial and a university context. The automatic detection yields an average precision of 59 at an average recall of 82 with high variation. The evaluation in practical environments indicates benefits such as an increase of the awareness of quality defects. Yet, some smells were not clearly distinguishable. Lightweight smell detection can uncover many practically relevant requirements defects in a reasonably precise way. Although some smells need to be defined more clearly, smell detection provides a helpful means to support quality assurance in requirements engineering, for instance, as a supplement to reviews.
One specific area of QA is avoiding redundancy and cloning. Whereas @cite_2 use ConQAT to search for syntactic identity resulting from a copy-and-paste reuse, @cite_25 aim at detecting similar content, therefore using methods from information retrieval (such as Latent Semantic Analysis @cite_57 ). @cite_51 extend this work specifically for use cases. Their tool, ReqAlign, classifies each step with a semantic abstraction of the step. These publications analyze the performance of their approaches, and depending on the artifact and methods achieve precision and recall close to 1 (see Table ).
@cite_57 @cite_51 @cite_25 @cite_2
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We develop a semantic parsing framework based on semantic similarity for open domain question answering (QA). We focus on single-relation questions and decompose each question into an entity mention and a relation pattern. Using convolutional neural network models, we measure the similarity of entity mentions with entities in the knowledge base (KB) and the similarity of relation patterns and relations in the KB. We score relational triples in the KB using these measures and select the top scoring relational triple to answer the question. When evaluated on an open-domain QA task, our method achieves higher precision across different recall points compared to the previous approach, and can improve F1 by 7 points. Community-based Question Answering (cQA) is a popular online service where users can ask and answer questions on any topics. This paper is concerned with the problem of question retrieval. Question retrieval in cQA aims to find historical questions that are semantically equivalent or relevant to the queried questions. Although the translation-based language model (, 2008) has gained the state-of-the-art performance for question retrieval, they ignore the latent topic information in calculating the semantic similarity between questions. In this paper, we propose a topic model incorporated with the category information into the process of discovering the latent topics in the content of questions. Then we combine the semantic similarity based latent topics with the translation-based language model into a unified framework for question retrieval. Experiments are carried out on a real world cQA data set from Yahoo! Answers. The results show that our proposed method can significantly improve the question retrieval performance of translation-based language model. A standard approach to cross-language information retrieval (CLIR) uses Latent Semantic Analysis (LSA) in conjunction with a multilingual parallel aligned corpus. This approach has been shown to be successful in identifying similar documents across languages - or more precisely, retrieving the most similar document in one language to a query in another language. However, the approach has severe drawbacks when applied to a related task, that of clustering documents "language-independently", so that documents about similar topics end up closest to one another in the semantic space regardless of their language. The problem is that documents are generally more similar to other documents in the same language than they are to documents in a different language, but on the same topic. As a result, when using multilingual LSA, documents will in practice cluster by language, not by topic. We propose a novel application of PARAFAC2 (which is a variant of PARAFAC, a multi-way generalization of the singular value decomposition [SVD]) to overcome this problem. Instead of forming a single multilingual term-by-document matrix which, under LSA, is subjected to SVD, we form an irregular three-way array, each slice of which is a separate term-by-document matrix for a single language in the parallel corpus. The goal is to compute an SVD for each language such that V (the matrix of right singular vectors) is the same across all languages. Effectively, PARAFAC2 imposes the constraint, not present in standard LSA, that the "concepts" in all documents in the parallel corpus are the same regardless of language. Intuitively, this constraint makes sense, since the whole purpose of using a parallel corpus is that exactly the same concepts are expressed in the translations. We tested this approach by comparing the performance of PARAFAC2 with standard LSA in solving a particular CLIR problem. From our results, we conclude that PARAFAC2 offers a very promising alternative to LSA not only for multilingual document clustering, but also for solving other problems in cross-language information retrieval. We are concerned by the use of factorial correspondence analysis (FCA) for image retrieval. FCA is designed for analyzing contingency tables. In textual data analysis (TDA), FCA analyzes a contingency table crossing terms words and documents. To adapt FCA on images, we first define "visual words" computed from scalable invariant feature transform (SIFT) descriptors in images and use them for image quantization. At this step, we can build a contingency table crossing "visual words" as terms words and images as documents. The method was tested on the Caltech4 and Stewenius and Nister datasets on which it provides better results (quality of results and execution time) than classical methods as tf * idf and probabilistic latent semantic analysis (PLSA). To scale up and improve the retrieval quality, we propose a new retrieval schema using inverted files based on the relevant indicators of correspondence analysis (representation quality of images on axes and contribution of images to the inertia of the axes). The numerical experiments show that our algorithm performs faster than the exhaustive method without losing precision. The Web offers fast and easy access to a wide range of documents in various languages, and translation and editing tools provide the means to create derivative documents fairly easily. This leads to the need to develop effective tools for detecting cross-language plagiarism. Given a suspicious document, cross-language plagiarism detection comprises two main subtasks: retrieving documents that are candidate sources for that document and analyzing those candidates one by one to determine their similarity to the suspicious document. In this paper we focus on the second subtask and introduce a novel approach for assessing cross-language similarity between texts for detecting plagiarized cases. Our proposed approach has two main steps: a vector-based retrieval framework that focuses on high recall, followed by a more precise similarity analysis based on dynamic text alignment. Experiments show that our method outperforms the methods of the best results in PAN-2012 and PAN-2014 in terms of plagdet score. We also show that aligning n-gram units, instead of aligning complete sentences, improves the accuracy of detecting plagiarism. Research has seen considerable achievements concerning translation of natural language patterns into formal queries for Question Answering (QA) based on Knowledge Graphs (KG). One of the main challenges in this research area is about how to identify which property within a Knowledge Graph matches the predicate found in a Natural Language (NL) relation. Current approaches for formal query generation attempt to resolve this problem mainly by first retrieving the named entity from the KG together with a list of its predicates, then filtering out one from all the predicates of the entity. We attempt an approach to directly match an NL predicate to KG properties that can be employed within QA pipelines. In this paper, we specify a systematic approach as well as providing a tool that can be employed to solve this task. Our approach models KB relations with their underlying parts of speech, we then enhance this with extra attributes obtained from Wordnet and Dependency parsing characteristics. From a question, we model a similar representation of query relations. We then define distance measurements between the query relation and the properties representations from the KG to identify which property is referred to by the relation within the query. We report substantive recall values and considerable precision from our evaluation. A popular recent approach to answering open-domain questions is to first search for question-related passages and then apply reading comprehension models to extract answers. Existing methods usually extract answers from single passages independently. But some questions require a combination of evidence from across different sources to answer correctly. In this paper, we propose two models which make use of multiple passages to generate their answers. Both use an answer-reranking approach which reorders the answer candidates generated by an existing state-of-the-art QA model. We propose two methods, namely, strength-based re-ranking and coverage-based re-ranking, to make use of the aggregated evidence from different passages to better determine the answer. Our models have achieved state-of-the-art results on three public open-domain QA datasets: Quasar-T, SearchQA and the open-domain version of TriviaQA, with about 8 percentage points of improvement over the former two datasets. The authors address the issue of reasoning with two classes of commonly used semantic integrity constraints in database and knowledge-base systems: implication constraints and referential constraints. They first consider a central problem in this respect, the IRC-refuting problem, which is to decide whether a conjunctive query always produces an empty relation on (finite) database instances satisfying a given set of implication and referential constraints. Since the general problem is undecidable, they only consider acyclic referential constraints. Under this assumption, they prove that the IRC-refuting problem is decidable, and give a novel necessary and sufficient condition for it. Under the same assumption, they also study several other problems encountered in semantic query optimization, such as the semantics-based query containment problem, redundant join problem, and redundant selection-condition problem, and show that they are polynomially equivalent or reducible to the IRC-refuting problem. Moreover, they give results on reducing the complexity for some special cases of the IRC-refuting problem. The major challenge for Question Retrieval (QR) in Community Question Answering (CQA) is the lexical gap between the queried question and the historical questions. This paper proposes a novel Question-Answer Topic Model (QATM) to learn the latent topics aligned across the question-answer pairs to alleviate the lexical gap problem, with the assumption that a question and its paired answer share the same topic distribution. Experiments conducted on a real world CQA dataset from Yahoo! Answers show that combining both parts properly can get more knowledge than each part or both parts in a simple mixing way and combining our QATM with the state-of-the-art translation-based language model, where the topic and translation information is learned from the question-answer pairs at two different grained semantic levels respectively, can significantly improve the QR performance. Directly reading documents and being able to answer questions from them is an unsolved challenge. To avoid its inherent difficulty, question answering (QA) has been directed towards using Knowledge Bases (KBs) instead, which has proven effective. Unfortunately KBs often suffer from being too restrictive, as the schema cannot support certain types of answers, and too sparse, e.g. Wikipedia contains much more information than Freebase. In this work we introduce a new method, Key-Value Memory Networks, that makes reading documents more viable by utilizing different encodings in the addressing and output stages of the memory read operation. To compare using KBs, information extraction or Wikipedia documents directly in a single framework we construct an analysis tool, WikiMovies, a QA dataset that contains raw text alongside a preprocessed KB, in the domain of movies. Our method reduces the gap between all three settings. It also achieves state-of-the-art results on the existing WikiQA benchmark. The rapidly increasing popularity of community-based Question Answering (cQA) services, e.g. Yahoo! Answers, Baidu Zhidao, etc. have attracted great attention from both academia and industry. Besides the basic problems, like question searching and answer finding, it should be noted that the low participation rate of users in cQA service is the crucial problem which limits its development potential. In this paper, we focus on addressing this problem by recommending answer providers, in which a question is given as a query and a ranked list of users is returned according to the likelihood of answering the question. Based on the intuitive idea for recommendation, we try to introduce topic-level model to improve heuristic term-level methods, which are treated as the baselines. The proposed approach consists of two steps: (1) discovering latent topics in the content of questions and answers as well as latent interests of users to build user profiles; (2) recommending question answerers for new arrival questions based on latent topics and term-level model. Specifically, we develop a general generative model for questions and answers in cQA, which is then altered to obtain a novel computationally tractable Bayesian network model. Experiments are carried out on a real-world data crawled from Yahoo! Answers during Jun 12 2007 to Aug 04 2007, which consists of 118510 questions, 772962 answers and 150324 users. The experimental results reveal significant improvements over the baseline methods and validate the positive influence of topic-level information. Person re-identification is an important technique towards automatic search of a person's presence in a surveillance video. Two fundamental problems are critical for person re-identification, feature representation and metric learning. An effective feature representation should be robust to illumination and viewpoint changes, and a discriminant metric should be learned to match various person images. In this paper, we propose an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA). The LOMO feature analyzes the horizontal occurrence of local features, and maximizes the occurrence to make a stable representation against viewpoint changes. Besides, to handle illumination variations, we apply the Retinex transform and a scale invariant texture operator. To learn a discriminant metric, we propose to learn a discriminant low dimensional subspace by cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is learned on the derived subspace. We also present a practical computation method for XQDA, as well as its regularization. Experiments on four challenging person re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show that the proposed method improves the state-of-the-art rank-1 identification rates by 2.2 , 4.88 , 28.91 , and 31.55 on the four databases, respectively. Person re-identification is an important technique towards automatic search of a person's presence in a surveillance video. Two fundamental problems are critical for person re-identification, feature representation and metric learning. An effective feature representation should be robust to illumination and viewpoint changes, and a discriminant metric should be learned to match various person images. In this paper, we propose an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA). The LOMO feature analyzes the horizontal occurrence of local features, and maximizes the occurrence to make a stable representation against viewpoint changes. Besides, to handle illumination variations, we apply the Retinex transform and a scale invariant texture operator. To learn a discriminant metric, we propose to learn a discriminant low dimensional subspace by cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is learned on the derived subspace. We also present a practical computation method for XQDA, as well as its regularization. Experiments on four challenging person re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show that the proposed method improves the state-of-the-art rank-1 identification rates by 2.2 , 4.88 , 28.91 , and 31.55 on the four databases, respectively. Cross-language plagiarism detection deals with the automatic identification and extraction of plagiarism in a multilingual setting. In this setting, a suspicious document is given, and the task is to retrieve all sections from the document that originate from a large, multilingual document collection. Our contributions in this field are as follows: (1) a comprehensive retrieval process for cross-language plagiarism detection is introduced, highlighting the differences to monolingual plagiarism detection, (2) state-of-the-art solutions for two important subtasks are reviewed, (3) retrieval models for the assessment of cross-language similarity are surveyed, and, (4) the three models CL-CNG, CL-ESA and CL-ASA are compared. Our evaluation is of realistic scale: it relies on 120,000 test documents which are selected from the corpora JRC-Acquis and Wikipedia, so that for each test document highly similar documents are available in all of the six languages English, German, Spanish, French, Dutch, and Polish. The models are employed in a series of ranking tasks, and more than 100 million similarities are computed with each model. The results of our evaluation indicate that CL-CNG, despite its simple approach, is the best choice to rank and compare texts across languages if they are syntactically related. CL-ESA almost matches the performance of CL-CNG, but on arbitrary pairs of languages. CL-ASA works best on "exact" translations but does not generalize well. Conjunctive queries (CQs) fail to provide an answer when the pattern described by the query does not exactly match the data. CQs might thus be too restrictive as a querying mechanism when data is semistructured or incomplete. The semantic web therefore provides a formalism - known as well-designed pattern trees (WDPTs) - that tackles this problem: WDPTs allow us to match patterns over the data, if available, but do not fail to give an answer otherwise. Here we abstract away the specifics of semantic web applications and study WDPTs over arbitrary relational schemas. Our language properly subsumes the class of CQs. Hence, WDPT evaluation is intractable. We identify structural proper ties of WDPTs that lead to tractability of various variants of the evaluation problem. For checking if a WDPT is equivalent to one in our tractable class, we prove 2EXPTIME-membership. As a corollary, we obtain fixed-parameter tractability of (variants of) the evaluation problem. Our techniques also allow us to develop a theory of approximations for WDPTs. We consider the problem of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Our proposed solution trains models to produce well calibrated confidence scores for their results on individual paragraphs. We sample multiple paragraphs from the documents during training, and use a shared-normalization training objective that encourages the model to produce globally correct output. We combine this method with a state-of-the-art pipeline for training models on document QA data. Experiments demonstrate strong performance on several document QA datasets. Overall, we are able to achieve a score of 71.3 F1 on the web portion of TriviaQA, a large improvement from the 56.7 F1 of the previous best system. An information retrieval technique, latent semantic indexing, is used to automatically identify traceability links from system documentation to program source code. The results of two experiments to identify links in existing software systems (i.e., the LEDA library, and Albergate) are presented. These results are compared with other similar type experimental results of traceability link identification using different types of information retrieval techniques. The method presented proves to give good results by comparison and additionally it is a low cost, highly flexible method to apply with regards to preprocessing and or parsing of the source code and documentation. Question Answering (QA) is retrieving answers to natural language questions from a collection of documents rather than retrieving relevant documents containing the keywords of the query which is performed by search engines. What a user usually wants is often a precise answer to a question. For example, given the question “Who won the nobel prize in peace in 2006?” what a user really wants is the answer “Dr. Muhammad Yunus”, in stead of reading through lots of documents that contain the words “win”, “nobel”,“prize”, “peace” and “2006” etc. This means that question answering systems will possibly be integral to the next generation of search engines. The Text Retrieval Conference, TREC1 QA track is the major large-scale evaluation environment for open-domain question answering systems. The questions in the TREC-2007 QA track are clustered by target, which is the overall theme or topic of the questions. The track has three types of questions: 1. factoid that require only one correct response, 2. list that require a non redundant list of correct responses and 3. other questions that require a non redundant list of facts about the target that has not already been discovered by a previous answer. We took the approach of designing a question answering system that is based on document tagging and question classification. Question classification extracts useful information (i.e. answer type) from the question about how to answer the question. Document tagging extracts useful information from the documents, which will be used in finding the answer to the question. We used different available tools to tag the documents. Our system classifies the questions using manually developed rules. We consider the problem of open-domain question answering (Open QA) over massive knowledge bases (KBs). Existing approaches use either manually curated KBs like Freebase or KBs automatically extracted from unstructured text. In this paper, we present OQA, the first approach to leverage both curated and extracted KBs. A key technical challenge is designing systems that are robust to the high variability in both natural language questions and massive KBs. OQA achieves robustness by decomposing the full Open QA problem into smaller sub-problems including question paraphrasing and query reformulation. OQA solves these sub-problems by mining millions of rules from an unlabeled question corpus and across multiple KBs. OQA then learns to integrate these rules by performing discriminative training on question-answer pairs using a latent-variable structured perceptron algorithm. We evaluate OQA on three benchmark question sets and demonstrate that it achieves up to twice the precision and recall of a state-of-the-art Open QA system. This paper studies the problem of question retrieval in community question answering (CQA). To bridge lexical gaps in questions, which is regarded as the biggest challenge in retrieval, state-of-the-art methods learn translation models using answers under an assumption that they are parallel texts. In practice, however, questions and answers are far from "parallel". Indeed, they are heterogeneous for both the literal level and user behaviors. There are a particularly large number of low quality answers, to which the performance of translation models is vulnerable. To address these problems, we propose a supervised question-answer topic modeling approach. The approach assumes that questions and answers share some common latent topics and are generated in a "question language" and "answer language" respectively following the topics. The topics also determine an answer quality signal. Compared with translation models, our approach not only comprehensively models user behaviors on CQA portals, but also highlights the instinctive heterogeneity of questions and answers. More importantly, it takes answer quality into account and performs robustly against noise in answers. With the topic modeling approach, we propose a topic-based language model, which matches questions not only on a term level but also on a topic level. We conducted experiments on large scale data from Yahoo! Answers and Baidu Knows. Experimental results show that the proposed model can significantly outperform state-of-the-art retrieval models in CQA.