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.
In summary, in our contribution, we extend the current state of reported evidence on automatic QA for requirements artifacts via systematic studies in terms of distribution, precision, recall, and relevance, as well as by means of a systematic evaluation with practitioners under realistic conditions. We perform this on both existing, as well as new quality defects taken from the ISO 29148. Therefore, we extend our previously published first empirical steps @cite_41 to close these gaps by thorough empirical evaluation.
@cite_41
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There is a documented gap between academic and practitioner views on software testing. This paper tries to close the gap by investigating both views regarding the benefits and limits of test automation. The academic views are studied with a systematic literature review while the practitioners views are assessed with a survey, where we received responses from 115 software professionals. The results of the systematic literature review show that the source of evidence regarding benefits and limitations is quite shallow as only 25 papers provide the evidence. Furthermore, it was found that benefits often originated from stronger sources of evidence (experiments and case studies), while limitations often originated from experience reports. We believe that this is caused by publication bias of positive results. The survey showed that benefits of test automation were related to test reusability, repeatability, test coverage and effort saved in test executions. The limitations were high initial invests in automation setup, tool selection and training. Additionally, 45 of the respondents agreed that available tools in the market offer a poor fit for their needs. Finally, it was found that 80 of the practitioners disagreed with the vision that automated testing would fully replace manual testing. Abstract Context For many years, we have observed industry struggling in defining a high quality requirements engineering (RE) and researchers trying to understand industrial expectations and problems. Although we are investigating the discipline with a plethora of empirical studies, they still do not allow for empirical generalisations. Objective To lay an empirical and externally valid foundation about the state of the practice in RE, we aim at a series of open and reproducible surveys that allow us to steer future research in a problem-driven manner. Method We designed a globally distributed family of surveys in joint collaborations with different researchers and completed the first run in Germany. The instrument is based on a theory in the form of a set of hypotheses inferred from our experiences and available studies. We test each hypothesis in our theory and identify further candidates to extend the theory by correlation and Grounded Theory analysis. Results In this article, we report on the design of the family of surveys, its underlying theory, and the full results obtained from Germany with participants from 58 companies. The results reveal, for example, a tendency to improve RE via internally defined qualitative methods rather than relying on normative approaches like CMMI. We also discovered various RE problems that are statistically significant in practice. For instance, we could corroborate communication flaws or moving targets as problems in practice. Our results are not yet fully representative but already give first insights into current practices and problems in RE, and they allow us to draw lessons learnt for future replications. Conclusion Our results obtained from this first run in Germany make us confident that the survey design and instrument are well-suited to be replicated and, thereby, to create a generalisable empirical basis of RE in practice. Objectives: Evidence-based medicine depends on the timely synthesis of research findings. An important source of synthesized evidence resides in systematic reviews. However, a bottleneck in review production involves dual screening of citations with titles and abstracts to find eligible studies. For this research, we tested the effect of various kinds of textual information (features) on performance of a machine learning classifier. Based on our findings, we propose an automated system to reduce screeing burden, as well as offer quality assurance. Methods: We built a database of citations from 5 systematic reviews that varied with respect to domain, topic, and sponsor. Consensus judgments regarding eligibility were inferred from published reports. We extracted 5 feature sets from citations: alphabetic, alphanumeric +, indexing, features mapped to concepts in systematic reviews, and topic models. To simulate a two-person team, we divided the data into random halves. We optimized the parameters of a Bayesian classifier, then trained and tested models on alternate data halves. Overall, we conducted 50 independent tests. Results: All tests of summary performance (mean F3) surpassed the corresponding baseline, P<0.0001. The ranks for mean F3, precision, and classification error were statistically different across feature sets averaged over reviews; P-values for Friedman's test were .045, .002, and .002, respectively. Differences in ranks for mean recall were not statistically significant. Alphanumeric+ features were associated with best performance; mean reduction in screening burden for this feature type ranged from 88 to 98 for the second pass through citations and from 38 to 48 overall. Conclusions: A computer-assisted, decision support system based on our methods could substantially reduce the burden of screening citations for systematic review teams and solo reviewers. Additionally, such a system could deliver quality assurance both by confirming concordant decisions and by naming studies associated with discordant decisions for further consideration. © 2014 Quality assurance (QA) tasks, such as testing, profiling, and performance evaluation, have historically been done in-house on developer-generated workloads and regression suites. Since this approach is inadequate for many systems, tools and processes are being developed to improve software quality by increasing user participation in the QA process. A limitation of these approaches is that they focus on isolated mechanisms, not on the coordination and control policies and tools needed to make the global QA process efficient, effective, and scalable. To address these issues, we have initiated the Skoll project, which is developing and validating novel software QA processes and tools that leverage the extensive computing resources of worldwide user communities in a distributed, continuous manner to significantly and rapidly improve software quality. This paper provides several contributions to the study of distributed continuous QA. First, it illustrates the structure and functionality of a generic around-the-world, around-the-clock QA process and describes several sophisticated tools that support this process. Second, it describes several QA scenarios built using these tools and process. Finally, it presents a feasibility study applying these scenarios to a 1MLOC+ software package called ACE+TAO. While much work remains to be done, the study suggests that the Skoll process and tools effectively manage and control distributed, continuous QA processes. Using Skoll we rapidly identified problems that had taken the ACE+TAO developers substantially longer to find and several of which had previously not been found. Moreover, automatic analysis of QA task results often provided developers information that quickly led them to the root cause of the problems. Common benchmark data sets, standardized performance metrics, and baseline algorithms have demonstrated considerable impact on research and development in a variety of application domains. These resources provide both consumers and developers of technology with a common framework to objectively compare the performance of different algorithms and algorithmic improvements. In this paper, we present such a framework for evaluating object detection and tracking in video: specifically for face, text, and vehicle objects. This framework includes the source video data, ground-truth annotations (along with guidelines for annotation), performance metrics, evaluation protocols, and tools including scoring software and baseline algorithms. For each detection and tracking task and supported domain, we developed a 50-clip training set and a 50-clip test set. Each data clip is approximately 2.5 minutes long and has been completely spatially temporally annotated at the I-frame level. Each task domain, therefore, has an associated annotated corpus of approximately 450,000 frames. The scope of such annotation is unprecedented and was designed to begin to support the necessary quantities of data for robust machine learning approaches, as well as a statistically significant comparison of the performance of algorithms. The goal of this work was to systematically address the challenges of object detection and tracking through a common evaluation framework that permits a meaningful objective comparison of techniques, provides the research community with sufficient data for the exploration of automatic modeling techniques, encourages the incorporation of objective evaluation into the development process, and contributes useful lasting resources of a scale and magnitude that will prove to be extremely useful to the computer vision research community for years to come. The paper presents an adaptive approach to perform impact analysis from a given change request to source code. Given a textual change request (e.g., a bug report), a single snapshot (release) of source code, indexed using Latent Semantic Indexing, is used to estimate the impact set. Should additional contextual information be available, the approach configures the best-fit combination to produce an improved impact set. Contextual information includes the execution trace and an initial source code entity verified for change. Combinations of information retrieval, dynamic analysis, and data mining of past source code commits are considered. The research hypothesis is that these combinations help counter the precision or recall deficit of individual techniques and improve the overall accuracy. The tandem operation of the three techniques sets it apart from other related solutions. Automation along with the effective utilization of two key sources of developer knowledge, which are often overlooked in impact analysis at the change request level, is achieved. To validate our approach, we conducted an empirical evaluation on four open source software systems. A benchmark consisting of a number of maintenance issues, such as feature requests and bug fixes, and their associated source code changes was established by manual examination of these systems and their change history. Our results indicate that there are combinations formed from the augmented developer contextual information that show statistically significant improvement over stand-alone approaches. We apply a general deep learning framework to address the non-factoid question answering task. Our approach does not rely on any linguistic tools and can be applied to different languages or domains. Various architectures are presented and compared. We create and release a QA corpus and setup a new QA task in the insurance domain. Experimental results demonstrate superior performance compared to the baseline methods and various technologies give further improvements. For this highly challenging task, the top-1 accuracy can reach up to 65.3 on a test set, which indicates a great potential for practical use. We apply a general deep learning framework to address the non-factoid question answering task. Our approach does not rely on any linguistic tools and can be applied to different languages or domains. Various architectures are presented and compared. We create and release a QA corpus and setup a new QA task in the insurance domain. Experimental results demonstrate superior performance compared to the baseline methods and various technologies give further improvements. For this highly challenging task, the top-1 accuracy can reach up to 65.3 on a test set, which indicates a great potential for practical use. We present an assessment of the state and historic development of evaluation practices as reported in papers published at the IEEE Visualization conference. Our goal is to reflect on a meta-level about evaluation in our community through a systematic understanding of the characteristics and goals of presented evaluations. For this purpose we conducted a systematic review of ten years of evaluations in the published papers using and extending a coding scheme previously established by [2012]. The results of our review include an overview of the most common evaluation goals in the community, how they evolved over time, and how they contrast or align to those of the IEEE Information Visualization conference. In particular, we found that evaluations specific to assessing resulting images and algorithm performance are the most prevalent (with consistently 80-90 of all papers since 1997). However, especially over the last six years there is a steady increase in evaluation methods that include participants, either by evaluating their performances and subjective feedback or by evaluating their work practices and their improved analysis and reasoning capabilities using visual tools. Up to 2010, this trend in the IEEE Visualization conference was much more pronounced than in the IEEE Information Visualization conference which only showed an increasing percentage of evaluation through user performance and experience testing. Since 2011, however, also papers in IEEE Information Visualization show such an increase of evaluations of work practices and analysis as well as reasoning using visual tools. Further, we found that generally the studies reporting requirements analyses and domain-specific work practices are too informally reported which hinders cross-comparison and lowers external validity. Objectives: In this survey, we reviewed the current state of the art in biomedical QA (Question Answering), within a broader framework of semantic knowledge-based QA approaches, and projected directions for the future research development in this critical area of intersection between Artificial Intelligence, Information Retrieval, and Biomedical Informatics. Materials and methods: We devised a conceptual framework within which to categorize current QA approaches. In particular, we used ''semantic knowledge-based QA'' as a category under which to subsume QA techniques and approaches, both corpus-based and knowledge base (KB)-based, that utilize semantic knowledge-informed techniques in the QA process, and we further classified those approaches into three subcategories: (1) semantics-based, (2) inference-based, and (3) logic-based. Based on the framework, we first conducted a survey of open-domain or non-biomedical-domain QA approaches that belong to each of the three subcategories. We then conducted an in-depth review of biomedical QA, by first noting the characteristics of, and resources available for, biomedical QA and then reviewing medical QA approaches and biological QA approaches, in turn. The research articles reviewed in this paper were found and selected through online searches. Results: Our review suggested the following tasks ahead for the future research development in this area: (1) Construction of domain-specific typology and taxonomy of questions (biological QA), (2) Development of more sophisticated techniques for natural language (NL) question analysis and classification, (3) Development of effective methods for answer generation from potentially conflicting evidences, (4) More extensive and integrated utilization of semantic knowledge throughout the QA process, and (5) Incorporation of logic and reasoning mechanisms for answer inference. Conclusion: Corresponding to the growth of biomedical information, there is a growing need for QA systems that can help users better utilize the ever-accumulating information. Continued research toward development of more sophisticated techniques for processing NL text, for utilizing semantic knowledge, and for incorporating logic and reasoning mechanisms, will lead to more useful QA systems. To date, few attempts have been made to develop new methods and validate existing ones for automatic evaluation of discourse coherence in the noisy domain of learner texts. We present the first systematic analysis of several methods for assessing coherence under the framework of automated assessment (AA) of learner free-text responses. We examine the predictive power of different coherence models by measuring the effect on performance when combined with an AA system that achieves competitive results, but does not use discourse coherence features, which are also strong indicators of a learner's level of attainment. Additionally, we identify new techniques that outperform previously developed ones and improve on the best published result for AA on a publically-available dataset of English learner free-text examination scripts. This paper considers the framework of the so-called "market basket problem", in which a database of transactions is mined for the occurrence of unusually frequent item sets. In our case, "unusually frequent" involves estimates of the frequency of each item set divided by a baseline frequency computed as if items occurred independently. The focus is on obtaining reliable estimates of this measure of interestingness for all item sets, even item sets with relatively low frequencies. For example, in a medical database of patient histories, unusual item sets including the item "patient death" (or other serious adverse event) might hopefully be flagged with as few as 5 or 10 occurrences of the item set, it being unacceptable to require that item sets occur in as many as 0.1 of millions of patient reports before the data mining algorithm detects a signal. Similar considerations apply in fraud detection applications. Thus we abandon the requirement that interesting item sets must contain a relatively large fixed minimal support, and adopt a criterion based on the results of fitting an empirical Bayes model to the item set counts. The model allows us to define a 95 Bayesian lower confidence limit for the "interestingness" measure of every item set, whereupon the item sets can be ranked according to their empirical Bayes confidence limits. For item sets of size J > 2, we also distinguish between multi-item associations that can be explained by the observed J(J-1) 2 pairwise associations, and item sets that are significantly more frequent than their pairwise associations would suggest. Such item sets can uncover complex or synergistic mechanisms generating multi-item associations. This methodology has been applied within the U.S. Food and Drug Administration (FDA) to databases of adverse drug reaction reports and within AT&T to customer international calling histories. We also present graphical techniques for exploring and understanding the modeling results. [Context and motivation] Research on eliciting requirements from a large number of online reviews using automated means has focused on functional aspects. Assuring the quality of an app is vital for its success. This is why user feedback concerning quality issues should be considered as well [Question problem] But to what extent do online reviews of apps address quality characteristics? And how much potential is there to extract such knowledge through automation? [Principal ideas results] By tagging online reviews, we found that users mainly write about "usability" and "reliability", but the majority of statements are on a subcharacteristic level, most notably regarding "operability", "adaptability", "fault tolerance", and "interoperability". A set of 16 language patterns regarding "usability" correctly identified 1,528 statements from a large dataset far more efficiently than our manual analysis of a small subset. [Contribution] We found that statements can especially be derived from online reviews about qualities by which users are directly affected, although with some ambiguity. Language patterns can identify statements about qualities with high precision, though the recall is modest at this time. Nevertheless, our results have shown that online reviews are an unused Big Data source for quality requirements. This paper addresses the problem of automatically performing resource-bound analysis, which can help programmers understand the performance characteristics of their programs. We introduce a method for resource-bound inference that (i) is compositional, (ii) produces machine-checkable certificates of the resource bounds obtained, and (iii) features a sound mechanism for user interaction if the inference fails. The technique handles recursive procedures and has the ability to exploit any known program invariants. An experimental evaluation with an implementation in the tool Pastis shows that the new analysis is competitive with state-of-the-art resource-bound tools while also creating Coq certificates. This paper presents an empirical evaluation of the quality of publicly available large-scale knowledge resources. The study includes a wide range of manually and automatically derived large-scale knowledge resources. In order to establish a fair and neutral comparison, the quality of each knowledge resource is indirectly evaluated using the same method on a Word Sense Disambiguation task. The evaluation framework selected has been the Senseval-3 English Lexical Sample Task. The study empirically demonstrates that automatically acquired knowledge resources surpass both in terms of precision and recall the knowledge resources derived manually, and that the combination of the knowledge contained in these resources is very close to the most frequent sense classifier. As far as we know, this is the first time that such a quality assessment has been performed showing a clear picture of the current state-of-the-art of publicly available wide coverage semantic resources. 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 work provides a framework for learning sequential attention in real-world visual object recognition, using an architecture of three processing stages. The first stage rejects irrelevant local descriptors based on an information theoretic saliency measure, providing candidates for foci of interest (FOI). The second stage investigates the information in the FOI using a codebook matcher and providing weak object hypotheses. The third stage integrates local information via shifts of attention, resulting in chains of descriptor-action pairs that characterize object discrimination. A Q-learner adapts then from explorative search and evaluative feedback from entropy decreases on the attention sequences, eventually prioritizing shifts that lead to a geometry of descriptor-action scanpaths that is highly discriminative with respect to object recognition. The methodology is successfully evaluated on indoors (COIL-20 database) and outdoors (TSG-20 database) imagery, demonstrating significant impact by learning, outperforming standard local descriptor based methods both in recognition accuracy and processing time. We study the problem of answering questions about images in the harder setting, where the test questions and corresponding images contain novel objects, which were not queried about in the training data. Such setting is inevitable in real world-owing to the heavy tailed distribution of the visual categories, there would be some objects which would not be annotated in the train set. We show that the performance of two popular existing methods drop significantly (up to 28 ) when evaluated on novel objects cf. known objects. We propose methods which use large existing external corpora of (i) unlabeled text, i.e. books, and (ii) images tagged with classes, to achieve novel object based visual question answering. We do systematic empirical studies, for both an oracle case where the novel objects are known textually, as well as a fully automatic case without any explicit knowledge of the novel objects, but with the minimal assumption that the novel objects are semantically related to the existing objects in training. The proposed methods for novel object based visual question answering are modular and can potentially be used with many visual question answering architectures. We show consistent improvements with the two popular architectures and give qualitative analysis of the cases where the model does well and of those where it fails to bring improvements. Though very important in software engineering, linking artifacts of the same type (clone detection) or different types (traceability recovery) is extremely tedious, error-prone, and effort-intensive. Past research focused on supporting analysts with techniques based on Natural Language Processing (NLP) to identify candidate links. Because many NLP techniques exist and their performance varies according to context, it is crucial to define and use reliable evaluation procedures. The aim of this paper is to propose a set of seven principles for evaluating the performance of NLP techniques in identifying equivalent requirements. In this paper, we conjecture, and verify, that NLP techniques perform on a given dataset according to both ability and the odds of identifying equivalent requirements correctly. For instance, when the odds of identifying equivalent requirements are very high, then it is reasonable to expect that NLP techniques will result in good performance. Our key idea is to measure this random factor of the specific dataset(s) in use and then adjust the observed performance accordingly. To support the application of the principles we report their practical application to a case study that evaluates the performance of a large number of NLP techniques for identifying equivalent requirements in the context of an Italian company in the defense and aerospace domain. The current application context is the evaluation of NLP techniques to identify equivalent requirements. However, most of the proposed principles seem applicable to evaluating any estimation technique aimed at supporting a binary decision (e.g., equivalent nonequivalent), with the estimate in the range [0,1] (e.g., the similarity provided by the NLP), when the dataset(s) is used as a benchmark (i.e., testbed), independently of the type of estimator (i.e., requirements text) and of the estimation method (e.g., NLP). In many application domains of recommender systems, explicit rating information is sparse or non-existent. The preferences of the current user have therefore to be approximated by interpreting his or her behavior, i.e., the implicit user feedback. In the literature, a number of algorithm proposals have been made that rely solely on such implicit feedback, among them Bayesian Personalized Ranking (BPR). In the BPR approach, pairwise comparisons between the items are made in the training phase and an item i is considered to be preferred over item j if the user interacted in some form with i but not with j. In real-world applications, however, implicit feedback is not necessarily limited to such binary decisions as there are, e.g., different types of user actions like item views, cart or purchase actions and there can exist several actions for an item over time. In this paper we show how BPR can be extended to deal with such more fine-granular, graded preference relations. An empirical analysis shows that this extension can help to measurably increase the predictive accuracy of BPR on realistic e-commerce datasets.