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.
First, @cite_45 target the specific grammatical issue of coordination ambiguity (detecting problems of ambiguous references between parts of a sentence), mostly through statistical methods, such as occurrence and co-occurrence of words. In a case study, they report on a precision of their approach mostly between 54 , Krisch and Houdek @cite_32 , focus on the detection of passive voice and so-called weak words. They present their dictionary- and POS-based approach to practitioners and find many false positives, similar to our RQ 3. In average, a precision of 12 approaches focus on very related, but not identical quality violations or smells.
@cite_45 @cite_32
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In requirements engineering literature dealing with natural language specifications, we usually find writing rules like ‘avoid passive voice’ or ‘do not use weak words’. Adhering to such rules should result in understandable and unambiguous requirements. Passive voice, especially when used without an explicit actor, is considered to result in incomplete requirements. The usage of weak words is considered to result in imprecise requirements that are hardly testable. But is the inversion of the claim correct, i.e. does the violation of the writing rules result in problematic specifications? At least in our environment (the passenger car development of Mercedes-Benz) we observe that authors often use passive voice, and there are many requirements containing weak words. To answer this question, we conducted an empirical investigation whose results we report in this paper. The results of this investigation are quite surprising: The use of passive voice, even when the actor is missing, is almost never problematic, as the missing information (the actor) can in most cases easily derived from the context (i.e. surrounding requirements or the general project context). The usage of weak words may be considered problematic in approximately 12 of all occurrences. For an automatic analysis on weak words linguistic patterns can be defined to detect these problematic occurences. Abstract This article describes an algorithm called HyperLex that is capable of automatically determining word uses in a textbase without recourse to a dictionary. The algorithm makes use of the specific properties of word cooccurrence graphs, which are shown as having “small world” properties. Unlike earlier dictionary-free methods based on word vectors, it can isolate highly infrequent uses (as rare as 1 of all occurrences) by detecting “hubs” and high-density components in the cooccurrence graphs. The algorithm is applied here to information retrieval on the Web, using a set of highly ambiguous test words. An evaluation of the algorithm showed that it only omitted a very small number of relevant uses. In addition, HyperLex offers automatic tagging of word uses in context with excellent precision (97 , compared to 73 for baseline tagging, with an 82 recall rate). Remarkably good precision (96 ) was also achieved on a selection of the 25 most relevant pages for each use (including highly infrequent ones). Finally, HyperLex is combined with a graphic display technique that allows the user to navigate visually through the lexicon and explore the various domains detected for each word use. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . We report an empirical investigation on type-supervised domain adaptation for joint Chinese word segmentation and POS-tagging, making use of domainspecific tag dictionaries and only unlabeled target domain data to improve target-domain accuracies, given a set of annotated source domain sentences. Previous work on POS-tagging of other languages showed that type-supervision can be a competitive alternative to tokensupervision, while semi-supervised techniques such as label propagation are important to the effectiveness of typesupervision. We report similar findings using a novel approach for joint Chinese segmentation and POS-tagging, under a cross-domain setting. With the help of unlabeled sentences and a lexicon of 3,000 words, we obtain 33 error reduction in target-domain tagging. In addition, combined type- and token-supervision can lead to improved cost-effectiveness. In this paper we present preliminary results of a novel unsupervised approach for high-precision detection and correction of errors in the output of automatic speech recognition systems. We model the likely contexts of all words in an ASR system vocabulary by performing a lexical co-occurrence analysis using a large corpus of output from the speech system. We then identify regions in the data that contain likely contexts for a given query word. Finally, we detect words or sequences of words in the contextual regions that are unlikely to appear in the context and that are phonetically similar to the query word. Initial experiments indicate that this technique can produce high-precision targeted detection and correction of misrecognized query words. We present a novel technique that automatically alerts authors of requirements to the presence of potentially dangerous ambiguities. We first establish the notion of nocuous ambiguities, which are those that are likely to lead to misunderstandings. We test our approach on coordination ambiguities, which occur when words such as and and or are used. Our starting point is a dataset of ambiguous phrases from a requirements corpus and associated human judgements about their interpretation. We then use heuristics, based largely on word distribution information, to automatically replicate these judgements. The heuristics eliminate ambiguities which people interpret easily, leaving the nocuous ones to be analysed and rewritten by hand. We report on a series of experiments that evaluate our heuristics' performance against the human judgements. Many of our heuristics achieve high precision, and recall is greatly increased when they are used in combination. Word sense induction aims to discover different senses of a word from a corpus by using unsupervised learning approaches. Once a sense inventory is obtained for an ambiguous word, word sense discrimination approaches choose the best-fitting single sense for a given context from the induced sense inventory. However, there may not be a clear distinction between one sense and another, although for a context, more than one induced sense can be suitable. Graded word sense method allows for labeling a word in more than one sense. In contrast to the most common approach which is to apply clustering or graph partitioning on a representation of first or second order co-occurrences of a word, we propose a system that creates a substitute vector for each target word from the most likely substitutes suggested by a statistical language model. Word samples are then taken according to probabilities of these substitutes and the results of the co-occurrence model are clustered. This approach outperforms the other systems on graded word sense induction task in SemEval-2013. This paper describes the generation of a model capturing information on how placenames co-occur together. The advantages of the co-occurrence model over traditional gazetteers are discussed and the problem of placename disambiguation is presented as a case study. We begin by outlining the problem of ambiguous placenames. We demonstrate how analysis of Wikipedia can be used in the generation of a co-occurrence model. The accuracy of our model is compared to a handcrafted ground truth; then we evaluate alternative methods of applying this model to the disambiguation of placenames in free text (using the GeoCLEF evaluation forum). We conclude by showing how the inclusion of placenames in both the text and geographic parts of a query provides the maximum mean average precision and outline the benefits of a co-occurrence model as a data source for the wider field of geographic information retrieval (GIR). This paper presents a method for inducing the parts of speech of a language and part-of-speech labels for individual words from a large text corpus. Vector representations for the part-of-speech of a word are formed from entries of its near lexical neighbors. A dimensionality reduction creates a space representing the syntactic categories of unambiguous words. A neural net trained on these spatial representations classifies individual contexts of occurrence of ambiguous words. The method classifies both ambiguous and unambiguous words correctly with high accuracy. Chinese part-of-speech (POS) tagging assigns one POS tag to each word in a Chinese sentence. However, since words are not demarcated in a Chinese sentence, Chinese POS tagging requires word segmentation as a prerequisite. We could perform Chinese POS tagging strictly after word segmentation (one-at-a-time approach), or perform both word segmentation and POS tagging in a combined, single step simultaneously (all-atonce approach). Also, we could choose to assign POS tags on a word-by-word basis, making use of word features in the surrounding context (word-based), or on a character-by-character basis with character features (character-based). This paper presents an in-depth study on such issues of processing architecture and feature representation for Chinese POS tagging, within a maximum entropy framework. We found that while the all-at-once, characterbased approach is the best, the one-at-a-time, character-based approach is a worthwhile compromise, performing only slightly worse in terms of accuracy, but taking shorter time to train and run. As part of our investigation, we also built a state-of-the-art Chinese word segmenter, which outperforms the best SIGHAN 2003 word segmenters in the closed track on 3 out of 4 test corpora. Abbreviations and acronyms are widely used in the biomedical literature and many of them represent important biomedical concepts. Because many abbreviations are ambiguous (e.g., CAT denotes both chloramphenicol acetyl transferase and computed axial tomography, depending on the context), recognizing the full form associated with each abbreviation is in most cases equivalent to identifying the meaning of the abbreviation. This, in turn, allows us to perform more accurate natural language processing, information extraction, and retrieval. In this study, we have developed supervised approaches to identifying the full forms of ambiguous abbreviations within the context they appear. We first automatically assigned multiple possible full forms for each abbreviation; we then treated the in-context full-form prediction for each specific abbreviation occurrence as a case of word-sense disambiguation. We generated automatically a dictionary of all possible full forms for each abbreviation. We applied supervised machine-learning algorithms for disambiguation. Because some of the links between abbreviations and their corresponding full forms are explicitly given in the text and can be recovered automatically, we can use these explicit links to automatically provide training data for disambiguating the abbreviations that are not linked to a full form within a text. We evaluated our methods on over 150 thousand abstracts and obtain for coverage and precision results of 82p and 92p, respectively, when performed as tenfold cross-validation, and 79p and 80p, respectively, when evaluated against an external set of abstracts in which the abbreviations are not defined. In this paper we discuss our study of the problems 12 software companies experienced in software development. In total we present qualitative data collected from 45 focus groups that involved over 200 software staff. We look at how different practitioner groups respond to software process improvement problems. We show our classification and analysis of this data using correspondence analysis. Correspondence analysis is a graphical data representation method new to software development research. The aim of the work we present is to develop a more holistic understanding of the problems practitioners are experiencing in their attempts to improve their software processes. Our main finding is that there is an association between a company’s capability maturity and patterns of reported problems. Organizational problems are more associated with high maturity companies than with low maturity companies. Low maturity companies are closely linked to problems relating directly to projects such as documentation, timescales, tools and technology. Our findings also confirm differences in practitioner group problems. Senior managers cite problems with goals, culture and politics. Project managers are concerned with timescales, change management, budgets and estimates. Developers are experiencing problems with requirements, testing, documentation, communication, tools and technology. These associations are displayed graphically through correspondence analysis maps. This paper presents context-group discrimination, a disambiguation algorithm based on clustering. Senses are interpreted as groups (or clusters) of similar contexts of the ambiguous word. Words, contexts, and senses are represented in Word Space, a high-dimensional, real-valued space in which closeness corresponds to semantic similarity. Similarity in Word Space is based on second-order co-occurrence: two tokens (or contexts) of the ambiguous word are assigned to the same sense cluster if the words they co-occur with in turn occur with similar words in a training corpus. The algorithm is automatic and unsupervised in both training and application: senses are induced from a corpus without labeled training instances or other external knowledge sources. The paper demonstrates good performance of context-group discrimination for a sample of natural and artificial ambiguous words. We consider two of the most commonly cited measures of retrieval performance: average precision and R-precision. It is well known that average precision and R-precision are highly correlated and similarly robust measures of performance, though the reasons for this are not entirely clear. In this paper, we give a geometric argument which shows that under a very reasonable set of assumptions, average precision and R-precision both approximate the area under the precision-recall curve, thus explaining their high correlation. We further demonstrate through the use of TREC data that the similarity or difference between average precision and R-precision is largely governed by the adherence to, or violation of, these reasonable assumptions. We present a novel method for detecting instances of an object class or specific object in high-spatial-resolution optical remote sensing images. The proposed method integrates sparse representations for local-feature detection into generalized-Hough-transform object detection. Object parts are detected via class-specific sparse image representations of patches using learned target and background dictionaries, and their co-occurrence is spatially integrated by Hough voting, which enables object detection. We aim to efficiently detect target objects using a small set of positive training samples by matching essential object parts with a target dictionary while the residuals are explained by a background dictionary. Experimental results show that the proposed method achieves state-of-the-art performance for several examples including object-class detection and specific-object identification. The idea that at least some aspects of word meaning can be induced from patterns of word co-occurrence is becoming increasingly popular. However, there is less agreement about the precise computations involved, and the appropriate tests to distinguish between the various possibilities. It is important that the effect of the relevant design choices and parameter values are understood if psychological models using these methods are to be reliably evaluated and compared. In this article, we present a systematic exploration of the principal computational possibilities for formulating and validating representations of word meanings from word co-occurrence statistics. We find that, once we have identified the best procedures, a very simple approach is surprisingly successful and robust over a range of psychologically relevant evaluation measures. The evolution rate of mobile applications is much higher than regular software applications having shorter release deadlines and smaller code base. Mobile applications tend to be evolved quickly by developers to meet several new customer requirements and fix discovered bugs. However, evolving the existing features and design may introduce bad design practices, also called code smells, which can highly decrease the maintainability and performance of these mobile applications. However, unlike the area of object-oriented software systems, the detection of code smells in mobile applications received a very little of attention. Recent, few studies defined a set of quality metrics for Android applications and proposed a support to manually write a set of rules to detect code smells by combining these quality metrics. However, finding the best combination of metrics and their thresholds to identify code smells is left to the developer as a manual process. In this paper, we propose to automatically generate rules for the detection of code smells in Android applications using a multi-objective genetic programming algorithm (MOGP). The MOGP algorithm aims at finding the best set of rules that cover a set of code smell examples of Android applications based on two conflicting objective functions of precision and recall. We evaluate our approach on 184 Android projects with source code hosted in GitHub. The statistical test of our results show that the generated detection rules identified 10 Android smell types on these mobile applications with an average correctness higher than 82 and an average relevance of 77 based on the feedback of active developers of mobile apps. We describe Web-a-Where, a system for associating geography with Web pages. Web-a-Where locates mentions of places and determines the place each name refers to. In addition, it assigns to each page a geographic focus --- a locality that the page discusses as a whole. The tagging process is simple and fast, aimed to be applied to large collections of Web pages and to facilitate a variety of location-based applications and data analyses.Geotagging involves arbitrating two types of ambiguities: geo non-geo and geo geo. A geo non-geo ambiguity occurs when a place name also has a non-geographic meaning, such as a person name (e.g., Berlin) or a common word (Turkey). Geo geo ambiguity arises when distinct places have the same name, as in London, England vs. London, Ontario.An implementation of the tagger within the framework of the WebFountain data mining system is described, and evaluated on several corpora of real Web pages. Precision of up to 82 on individual geotags is achieved. We also evaluate the relative contribution of various heuristics the tagger employs, and evaluate the focus-finding algorithm using a corpus pretagged with localities, showing that as many as 91 of the foci reported are correct up to the country level. Translating into morphologically rich languages is a particularly difficult problem in machine translation due to the high degree of inflectional ambiguity in the target language, often only poorly captured by existing word translation models. We present a general approach that exploits source-side contexts of foreign words to improve translation prediction accuracy. Our approach is based on a probabilistic neural network which does not require linguistic annotation nor manual feature engineering. We report significant improvements in word translation prediction accuracy for three morphologically rich target languages. In addition, preliminary results for integrating our approach into a largescale English-Russian statistical machine translation system show small but statistically significant improvements in translation quality.