International Journal of Software Engineering and Knowledge Engineering

Sentiment analysis from microblog platform has received an increasing interest from web mining community in recent years. Current sentiment analysis methods are mainly based on the hypothesis that each word expresses only one sentiment. However, human sentiment are prototyped and fuzzy-confined as declared in social psychology, which is conflicting with the hypothesis. This is one of the barriers that impede the computation of complex public sentiment of web events in microblog. Therefore, how to find a reasonable computational model, combining learning technology and human sentiment cognition theory, is a novel idea in event sentiment analysis of microblog. In this paper, a new sentiment computation approach, which is defined as public sentiments discriminator (PSD), considering both fuzzy logic and sentiment complexity, is proposed. Unlike traditional machine learning methods, PSD is based on the rational hypothesis that sentiments are correlated with each other. A three-level computing structure, sentiment-term level, microblog level and public sentiment level, is employed. Experiments show that the proposed approach, PSD, can achieve similar accuracy and $F$1-measure but more cognitive results when compared with traditional well-known machine learning methods. These experimental studies have confirmed that PSD can generate an interpretable result with no restriction among sentiments.

Time series mining has become essential for extracting knowledge from the abundant data that flows out from many application domains. To overcome storage and processing challenges in time series mining, compression techniques are being used. In this paper, we investigate the loss/gain of performance of time series classification approaches when fed with lossy-compressed data. This extended empirical study is essential for reassuring practitioners, but also for providing more insights on how compression techniques can even be effective in smoothing and reducing noise in time series data. From a knowledge engineering perspective, we show that time series may be compressed by 90% using discrete wavelet transforms and still achieve remarkable classification accuracy, and that residual details left by popular wavelet compression techniques can sometimes even help to achieve higher classification accuracy than the raw time series data, as they better capture essential local features.

Clustering is the process of partitioning a dataset into groups based on the similarity between the instances. Many clustering algorithms were proposed, but none of them proved to provide good quality partition in all situations. Consensus clustering aims to enhance the clustering process by combining different partitions obtained from different algorithms to yield a better quality consensus solution. In this work, we propose a new consensus clustering method that uses a pattern mining technique in order to reduce the search space from instance-based into pattern-based space. Instead of finding one solution, our method generates multiple consensus candidates based on varying the number of base clusterings considered. The different solutions are then linked and presented as a tree that gives more insight about the similarities between the instances and the different partitions in the ensemble.

An important measure of clone detection performance is precision. However, there has been a marked lack of research into methods for efficiently and accurately measuring the precision of a clone detection tool. Instead, tool authors simply validate a small random sample of the clones their tools detected in a subject software system. Since there could be many thousands of clones reported by the tool, such a small random sample cannot guarantee an accurate and generalized measure of the tool’s precision for all the varieties of clones that can occur in any arbitrary software system. In this paper, we propose a machine-learning-based approach that can cluster similar clones together, and which can be used to maximize the variety of clones examined when measuring precision, while significantly reducing the biases a specific subject system has on the generality of the precision measured. Our technique reduces the efforts in measuring precision, while doubling the variety of clones validated and reducing biases that harm the generality of the measure by up to an order of magnitude. Our case study with the NiCad clone detector and the Java class library shows that our approach is effective in efficiently measuring an accurate and generalized precision of a subject clone detection tool.

Sequence-based software specification is a rigorous method for deriving a formal system model based on informal requirements, through a systematic process called sequence enumeration. Under this process, stimulus (input) sequences are considered in a breadth-first manner, with the expected system response to each sequence given. Not every sequence needs to be further extended by the enumeration rules. The completed specification encodes a Mealy machine and forms a basis for other activities including code development and testing. This paper presents a forward reduction algorithm for sequence-based specification. The need for such an algorithm has been identified by field applications. We used the state machine as an intermediate tool to comprehend and analyze all change impacts resulted from a forward reduction, and used an axiom system for its development. We present the algorithm both mathematically in functional form and procedurally in pseudocode, illustrate it with a symbolic example, and report a larger case study from the published literature in which the algorithm is applied. The algorithm will prove useful and effective in deriving a system-level specification as well as in merging and combining partial work products towards a formal system model in field applications.

DevOps is an emerging concept and methodology for bridging the gap in the process of software development. At present, applying DevOps to data analytical system (DAS) is increasingly embraced. But the characteristics of this system, such as data protection, always leads to a series of constrains. It results in more difficulty of conducting DevOps on DAS. Moreover, there are no DevOps solutions for reference. Therefore, exploring DevOps for DAS is valuable. In this paper, we illustrate DevOps demands of DAS from different perspectives, and constantly emphasize the importance of automation toolchain. Based on them, a process model for DAS DevOps (D²Ops) is proposed to clarify participants activities. In order to improve the efficiency, we attempt to integrate the automation toolchain. With the consideration of stability, six generic process components are designed to support this model. They can be the selection criteria for specific automation tools. We also present a reference facility based on these generic process components, and illustrate its implementation combining with a practical case. Furthermore, for a better D²Ops practice, the cross-cutting concerns are considered from the perspective of its data intensive trait.

With the growing in size and complexity of modern computer systems, the need for improving the quality at all stages of software development has become a critical issue. The current software production has been largely dependent on manual code development. Despite the slow development process, the errors introduced by the programmers contribute to a substantial portion of defects in the final software product. Model-driven engineering (MDE), despite having many advantages, is often overlooked by programmers due to lack of proper understanding and training in the matter. This paper investigates the advantages and disadvantages of MDE and looks at research results showing the adoption rates of design models. It analyzes different tools used for automated code generation and displays the reasons that led to technical decisions such as the programming language or design model used. In light of the findings, an educational tool, namely Lorini, was developed to provide automated code generation from the design models. The implemented tool consists of a plug-in for the Astah framework aimed at teaching Java programming to students through UML diagrams. It features instantaneous code generation from three types of UML diagrams, code-diagram matching, a feedback panel for error displays and on-the-fly compilation and execution of the resulting program. We also explore the possibility of generating assertion constraints from the design model and use them to verify the implementation. Evaluation of the tool indicated it to be successful with unique educational features and intuitive to use.

Developer profile plays an important role in software project planning, developer recommendation, personnel training, and other tasks. Modeling the ability and interest of developers is its key issue. However, most existing approaches require manual assessment, like 360${}^{\circ }$ performance evaluation. With the emergence of social networking sites such as StackOverflow and Github, a vast amount of developer information is created on a daily basis. Such personal and social context data has huge potential to support automatic and effective developer ability evaluation and interest mining. In this paper, we propose CPDScorer, a novel approach for modeling and scoring the programming ability and interest of developers through mining heterogeneous information from both community question answering (CQA) sites and open-source software (OSS) communities. CPDScorer analyzes the questions and answers posted in CQA sites, and evaluates the projects submitted in OSS communities to assign expertise scores as well as interest scores to developers, considering both the quantitative and qualitative factors. When profiling developer's ability and interest, a programming term extraction algorithm is also designed based on set covering. We have conducted experiments on StackOverflow and Github to measure the effectiveness of CPDScorer. The results show that our approach is feasible and practical in user programming ability and interest modeling. In particular, the precision of our approach reaches 80%.

Cross-project defect prediction (CPDP) has recently become very popular in the field of software defect prediction. It was generally treated as a binary classification problem or a regression problem in most of previous studies. However, these existing CPDP methods may be not suitable for those software projects that have limited manpower and budget. To address the issue of priority estimation for buggy software entities, in this paper CPDP is formulated as a ranking problem. Inspired by the idea of the pointwise approach to learning to rank, we propose a ranking-oriented CPDP approach called ROCPDP. A case study conducted on the datasets collected from AEEEM and PROMISE shows that ROCPDP outperforms the eight baseline methods in two CPDP scenarios, namely one-to-one and many-to-one. Besides, in the many-to-one scenario ROCPDP is, by and large, comparable to the best baseline method performed in a specific within-project defect prediction scenario.

The connection between traffic congestion and regional demand has become a consensus. A reasonable road network inflow control is essential to prevent the occurrence of congestion. With a consideration of deployment of detectors, an improved macroscopic fundamental diagram-based traffic volume dynamic control method via the feedback control is proposed in this study. Based on the origin–destination distribution of trips and connectivity of nodes, a novel method is adopted to identify the key nodes, on which the detectors are laid. An artificial neural network is adopted to predict traffic volume of those sections without detectors. As a case study, the proposed methodology is applied and estimated via simulation study to the regional road network which is located in the downtown area of Nanning, China. Adequate survey and analysis are carried out under the current road traffic conditions. It is proved that the regional traffic volume dynamic control can ensure steady and orderly regional traffic flow, and enhance the mobility during saturated traffic conditions.

Accurate real-time crash risk evaluation is essential for making prevention strategy in order to proactively improve traffic safety. Quite a number of models have been developed to evaluate traffic crash risk by using real-time surveillance data. In this paper, the basic idea of traffic safety region is introduced into highway crash risk evaluation. Sequential forward selection (SFS), principal components analysis (PCA) and least squares support vector machine (LSSVM) are used to estimate the traffic safety region and classify the traffic states (safe condition and unsafe condition). The proposed method works by first extracting state variables from the observed traffic variables. Two statistics $T^2$ and squared prediction error (SPE) are calculated by SFS–PCA and used as the final state variables for traffic state space. Next, LSSVM is used to estimate the boundary of traffic safety region and identify the traffic states in the traffic state space. To demonstrate the advantage of the proposed method, this study develops two crash risk evaluation models, namely SFS–LSSVM model and PCA–LSSVM model, based on crash data and non-crash data collected on freeway I-880N in Alameda. Validation results show that the method is of reasonably high accuracy for identifying traffic states.

Cross-project defect prediction trains a prediction model using historical data from source projects and applies the model to target projects. Most previous efforts assumed the cross-project data have the same metrics set, which means the metrics used and the size of metrics set are the same. However, this assumption may not hold in practical scenarios. In addition, software defect datasets have the class-imbalance problem which increases the difficulty for the learner to predict defects. In this paper, we advance canonical correlation analysis by deriving a joint feature space for associating cross-project data. We also propose a novel support vector machine algorithm which incorporates the correlation transfer information into classifier design for cross-project prediction. Moreover, we take different misclassification costs into consideration to make the classification inclining to classify a module as a defective one, alleviating the impact of imbalanced data. The experimental results show that our method is more effective compared to state-of-the-art methods.

The manual reading of all the product reviews to find a satisfying item is not only labor-intensive, but also tedious for the consumers. In this paper, we propose a feature-opinion mining approach to automatically summarize the reviews, which is based on dependency parsing. Specifically, in our approach we first utilize a regression model to generate sentiment word, including phrase and its sentiment weight, and then we extract the feature based on the dependency relationship between feature word and sentiment word, finally we assign a score to the feature according to the dependency relationship. The experimental results demonstrate that our approach can effectively mine the feature-opinion from reviews.

Bug triaging refers to the process of assigning a bug to the most appropriate fixer. As the scale and complexity of software increases, bug triaging becomes a tedious and time-consuming work. Existing bug triaging approaches typically treat it as a problem of optimizing recommendation accuracy. However, the time that different fixers may spend also varies. Thus, we take time cost as another optimizing objective aside from accuracy and use modern portfolio theory to strike a balance between them. In addition, for fixers with little fixing records, we need more data to build profiles about their expertise. To address these problems, we propose a bug triaging approach with awareness of accuracy and time cost, and we use bug reports from other projects to enrich the bug fixing history of fixers. We evaluate our approach with experiments on data collected from Bugzilla. The experiment results validate the effectiveness of our approach.

With the rapid advance of mobile computing technology and wireless networking, there is a significant increase of mobile applications (apps). This brings new business requirements and demands in mobile software testing, and causes new issues and challenges in mobile test automation. Existing mobile application testing approaches mostly concentrate on GUI-based testing, load and performance testing without considering large-scale concurrent mobile app test automation, and model-based test coverage analysis. In this paper, a mobile hierarchical GUI model is proposed to present mobile operation scenario flows and gesture features in a hierarchical manner, in order to facilitate test dependency analysis in test automation. Mobile app test coverage analysis is performed based on GUI ripping models. The paper also presents a developed system that provides a test automation solution using GUI models. Finally, the paper reports a case study to indicate the feasibility and effectiveness of the proposed approach.