Automatic program repair (APR) is a rapidly advancing field of software engineering that aims to supplement or replace manual bug fixing with an automated tool. For APR to be successfully adopted in industry, it is vital that APR tools respond to developer needs and preferences. However, very little research has considered developers' general attitudes to APR or developers' current bug fixing practices (the activity APR aims to replace). This paper responds to this gap by reporting on a survey of 386 software developers about their bug finding and fixing practices and experiences, and their instinctive attitudes towards APR. We find that bug finding and fixing is not necessarily as onerous for developers as has often been suggested, being rated as more satisfying than developers' general work. The fact that developers derive satisfaction and benefit from bug fixing indicates that APR adoption is not as simple as APR replacing an unwanted activity. When it comes to potential APR approaches, we find a strong preference for developers being kept in the loop (for example, choosing between different fixes or validating fixes) as opposed to a fully automated process. This suggests that advances in APR should be careful to consider the agency of the developer, as well as what information is presented to developers alongside fixes. It also indicates that there are key barriers related to trust that would need to be overcome for full scale APR adoption, supported by the fact that even those developers who stated that they were positive about APR listed several caveats and concerns. We find very few statistically significant relationships between particular demographic variables (for example, developer experience, age, education) and key attitudinal variables, suggesting that developers' instinctive attitudes towards APR are little influenced by experience level but are held widely across the developer community.

Automatic program repair (APR) offers significant potential for automating some coding tasks. Using APR could reduce the high costs historically associated with fixing code faults and deliver significant benefits to software engineering. Adopting APR could also have profound implications for software developers daily activities, transforming their work practices. To realise the benefits of APR it is vital that we consider how developers feel about APR and the impact APR may have on developers' work. Developing APR tools without consideration of the developer is likely to undermine the success of APR deployment. In this paper, we critically review how developers are considered in APR research by analysing how human factors are treated in 260 studies from Monperrus's Living Review of APR. Over half of the 260 studies in our review were motivated by a problem faced by developers (e.g., the difficulty associated with fixing faults). Despite these human-oriented motivations, fewer than 7% of the 260 studies included a human study. We looked in detail at these human studies and found their quality mixed (for example, one human study was based on input from only one developer). Our results suggest that software developers are often talked about in APR studies, but are rarely talked with. A more comprehensive and reliable understanding of developer human factors in relation to APR is needed. Without this understanding, it will be difficult to develop APR tools and techniques which integrate effectively into developers' workflows. We recommend a future research agenda to advance the study of human factors in APR.

Atrial fibrillation (AF) and ventricular arrhythmia (Arr) are among the most common and fatal cardiac arrhythmias in the world. Electrocardiogram (ECG) data, collected as part of the UK Biobank, represents an opportunity for analysis and classification of these two diseases in the UK. The main objective of our study is to investigate a two-stage model for the classification of individuals with AF and Arr in the UK Biobank dataset. The current literature addresses heart arrhythmia classification very extensively. However, the data used by most researchers lack enough instances of these common diseases. Moreover, by proposing the two-stage model and separation of normal and abnormal cases, we have improved the performance of the classifiers in detection of each specific disease. Our approach consists of two stages of classification. In the first stage, features of the ECG input are classified into two main classes: normal and abnormal. At the second stage, the features of the ECG are further categorised as abnormal and further classified into two diseases of AF and Arr. A diverse set of ECG features such as the QRS duration, PR interval and RR interval, as well as covariates such as sex, BMI, age and other factors, are used in the modelling process. For both stages, we use the XGBoost Classifier algorithm. The healthy population present in the data, has been undersampled to tackle the class imbalance present in the data. This technique has been applied and evaluated using an ECG dataset from the UKBioBank ECG taken at rest repository. The main results of our paper are as follows: The classification performance for the proposed approach has been measured using F1 score, Sensitivity (Recall) and Specificity (Precision). The results of the proposed system are 87.22%, 88.55% and 85.95%, for average F1 Score, average sensitivity and average specificity, respectively. Contribution and significance: The performance level indicates that automatic detection of AF and Arr in participants present in the UK Biobank is more precise and efficient if done in a two-stage manner. Automatic detection and classification of AF and Arr individuals this way would mean early diagnosis and prevention of more serious consequences later in their lives.

Over the last decade, particle swarm optimization has become increasingly sophisticated because well-balanced exploration and exploitation mechanisms have been proposed. The sequential quadratic programming method, which is widely used for real-parameter optimization problems, demonstrates its outstanding local search capability. In this study, two mechanisms are proposed and integrated into particle swarm optimization for single-objective numerical optimization. A novel ratio adaptation scheme is utilized for calculating the proportion of subpopulations and intermittently invoking the sequential quadratic programming for local search start from the best particle to seek a better solution. The novel particle swarm optimization variant was validated on CEC2013, CEC2014, and CEC2017 benchmark functions. The experimental results demonstrate impressive performance compared with the state-of-the-art particle swarm optimization-based algorithms. Furthermore, the results also illustrate the effectiveness of the two mechanisms when cooperating to achieve significant improvement.

Airports and their related operations have become the major bottlenecks to the entire air traffic management system, raising predictability, safety and environmental concerns. One of the underpinning techniques for digital and sustainable air transport is airport ground movement optimisation. Currently, real ground movement data is made freely available for the majority of aircraft at many airports. However, the recorded data is not accurate enough due to measurement errors and general uncertainties. In this paper, we aim to develop a new interval type-2 fuzzy logic based map matching algorithm, which can match each raw data point to the correct airport segment. To this aim, we first specifically design a set of interval type-2 Sugeno fuzzy rules and their associated rule weights, as well as the model output, based on preliminary experiments and sensitivity tests. Then, the fuzzy membership functions are fine-tuned by a particle swarm optimisation algorithm. Moreover, an extra checking step using the available data is further integrated to improve map matching accuracy. Using the real-world aircraft movement data at Hong Kong Airport, we compared the developed algorithm with other well-known map matching algorithms. Experimental results show that the designed interval type-2 fuzzy rules have the potential to handle map matching uncertainties, and the extra checking step can effectively improve map matching accuracy. The proposed algorithm is demonstrated to be robust and achieve the best map matching accuracy of over 96% without compromising the run time.

Researchers investigated the effects of three different previewing interventions on the oral reading rates of 12 junior and senior high school students with learning disabilities. Under fast-rate listening previewing (FRLP), students were instructed to follow silently as experimenters read from a text at an average rate that was 77.7% faster than the students' current oral reading rate. During slow-rate listening previewing (SRLP), students followed along as experimenters read at an average rate that was 22.5% faster than the students' reading rate. Students were instructed to read passages silently under silent previewing (SP). Immediately following each previewing intervention, students read the same passage aloud. The number of words read correctly per minute and the number of errors per minute served as dependent variables. The results showed statistically significant decreases in error rates under SRLP and SP. The results also showed that SRLP resulted in statistically significantly fewer errors per minute than FRLP. These results suggest that orally reading while students follow along at a rate much higher than their current reading rates may not be as beneficial as reading aloud at slower rates.