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Software testing plays a critical role in the development and the assurance of the software quality. However, the quality of the code that is responsible for testing may, itself, be affected by poor design choices, known as test smells. In the literature, test smells may be interpreted differently by developers, which in turn can lead to different detection tools and results. In our work, we have selected the mostly commonly used detection tools and have investigated their overall agreement across different projects and different test smells. The found results were evaluated according to the average level of agreement, where we observed a definite disagreement between the tools. To overcome this gap of misinterpretation, we propose in this paper a multi-label classification approach to detect test smells based on a deep representation of the test code. We conducted our experiments using 4 problem-transformation techniques and 4 ensemble techniques. To evaluate our experimental results, we built a benchmark using a tool-based approach for labelling and made it publicly available. Binary Relevance and RAkEL are found to be the best multi-label techniques that achieve high performance results.

Nurturing venture-capital-backed, high-growth entrepreneurship has been strongly promoted as an effective means to achieve local development in impoverished places. Yet growing evidence suggests th...

In this paper, we propose to use production executions to improve the quality of testing for certain methods of interest for developers. These methods can be methods that are not covered by the existing test suite, or methods that are poorly tested. We devise an approach called PANKTI which monitors applications as they execute in production, and then automatically generates differential unit tests, as well as derived oracles, from the collected data. PANKTI's monitoring and generation focuses on one single programming language, Java. We evaluate it on three real-world, open-source projects: a videoconferencing system, a PDF manipulation library, and an e-commerce application. We show that PANKTI is able to generate differential unit tests by monitoring target methods in production, and that the generated tests improve the quality of the test suite of the application under consideration.

Application Programming Interfaces (APIs) allow their users to reuse existing software functionality without implementing it by themselves. However, using external functionality can come at a cost. Because developers are decoupled from the API's inner workings, they face the possibility of misunderstanding, and therefore misusing APIs. Prior research has proposed state-of-the-art example-based API misuse detectors that rely on existing API usage examples mined from existing code bases. Intuitively, without a varied dataset of API usage examples, it is challenging for the example-based API misuse detectors to differentiate between infrequent but correct API usages and API misuses. Such mistakes lead to false positives in the API misuse detection results, which was reported in a recent study as a major limitation of the state-of-the-art. To tackle this challenge, in this paper, we first undertake a qualitative study of 384 falsely detected API misuses. We find that around one third of the false-positives are due to missing alternative correct API usage examples. Based on the knowledge gained from the qualitative study, we uncover five patterns which can be followed to generate artificial examples for complementing existing API usage examples in the API misuse detection. To evaluate the usefulness of the generated artificial examples, we apply a state-of-the-art example-based API misuse detector on 50 open source Java projects. We find that our artificial examples can complement the existing API usage examples by preventing the detection of 55 false API misuses. Furthermore, we conduct a pre-designed experiment in an automated API misuse detection benchmark (MUBench), in order to evaluate the impact of generated artificial examples on recall. We find that the API misuse detector covers the same true positive results with and without the artificial example, i.e., obtains the same recall of 94.7%. Our findings highlight the potential of improving API misuse detection by pattern-guided source code transformation techniques.

Assessing applicants for faculty positions in universities involves many issues. Each issue may involve a judgment based on uncertain or imprecise data. The uncertainty in data may exist in the interpretation made by the evaluator. This issue might lead to improper decision making. Modeling such a system using fuzzy logic will provide a more efficient model for handling imprecision. This article presents a fuzzy system for modeling the assessment of applicants for employment at academic universities. This system will utilize a multi-stage fuzzy model for measuring and evaluating the applicants. Utilizing fuzzy logic for applicants' evaluation will help administrators in choosing the best candidates for faculty positions. The fuzzy system was developed using jFuzzyLogic Java library. The reliability of the proposed system was proved by evaluating real-world case studies to prove its effectiveness to mimic human judgment. Moreover, the developed system has been evaluated by comparing it with a traditional mathematical method to prove the credibility and fairness of the proposed fuzzy system.

An energy efficient information and communication system is a need of the day. Information technology related industries are making efforts to reduce power consumption by improvising both the hardware infrastructure and software systems. The hardware is driven by software; hence, design and development of software may have a significant impact energy need of the overall system. Many times it is observed that complex software performs useless tasks leading to power consumption, referred to as energy leak. The solution of energy leaks for mobile applications has attracted the interest of researchers, but still handling energy leaks issues for applications developed using Java-based technology need to be explored. While designing software during maintenance phase, code smells are introduced which not only reduce performance of the software also may lead to execution of useless code thus enhancing the energy leak. The common practice of the software industry to remove the code smells is use of refactoring strategy. The basic idea of this study is to understand the impact of refactoring on total energy consumption. To explore this hypothesis a set of Java based applications are selected, code smell present in these applications are discovered and a suitable refactoring strategy is applied to reduce the code smell. Power consumption is estimated using an open source tool Microsoft Joulemeter, it monitors energy usage by machine resources and estimates power consumption. The energy consumption before and after refactoring is recorded and statistical t-test is performed to validate the proposed concept.

API misuses are well-known causes of software crashes and security vulnerabilities. However, their detection and repair is challenging given that the correct usages of (third-party) APIs might be obscure to the developers of client programs. This paper presents the first empirical study to assess the ability of existing automated bug repair tools to repair API misuses, which is a class of bugs previously unexplored. Our study examines and compares 14 Java test-suite-based repair tools (11 proposed before 2018, and three afterwards) on a manually curated benchmark (APIREPBENCH) consisting of 101 API misuses. We develop an extensible execution framework (APIARTY) to automatically execute multiple repair tools. Our results show that the repair tools are able to generate patches for 28% of the API misuses considered. While the 11 less recent tools are generally fast (the median execution time of the repair attempts is 3.87 minutes and the mean execution time is 30.79 minutes), the three most recent are less efficient (i.e., 98% slower) than their predecessors. The tools generate patches for API misuses that mostly belong to the categories of missing null check, missing value, missing exception, and missing call. Most of the patches generated by all tools are plausible (65%), but only few of these patches are semantically correct to human patches (25%). Our findings suggest that the design of future repair tools should support the localisation of complex bugs, including different categories of API misuses, handling of timeout issues, and ability to configure large software projects. Both APIREPBENCH and APIARTY have been made publicly available for other researchers to evaluate the capabilities of repair tools on detecting and fixing API misuses.

Traditional distribution of programming problems seems to create dissatisfaction among students. To overcome this problem, complexity measurement of programming problems is necessary. Although software is the outcome of human ingenious activity, cognitive informatics plays a significant role in understanding its ultimate characteristics. In this article, we have proposed a software complexity measurement algorithm based on cognitive weight of basic control structure that shrink the limitations of existing measures. Cognitive weight concept of any basic control structure are morally based on the rational capacity of Human Brain. According to cognitive data, we have developed a new software tool using Java Standard Edition and MySQL to measure the cognitive complexity by following our developed algorithm. This software is structured and developed based on the outcome of our research data which is capable of determining the complexity value of several programming languages. This will assist the instructors allocating the problems among the learners by preserving the equivalent level of difficulty. Therefore, the developed complexity measurement tool will ensure the students to get problems with identical difficulty level for assessment.

The current study is intended to explore the typology of other-initiated self-repair in term of its frequency of occurrence and to investigate the mechanism of self-repair in an academic setting. To obtain the required data, a classroom conversational analysis was conducted in which the researchers observed a group of graduate students (19 active EFL speakers) in a state university in East Java, Indonesia. Based on the qualitative data analysis, the results of the study revealed that all typologies of other-initiated selfrepair were used depending on the aims the interlocutors try to gain since all typologies have diverse aims and ways. Of all typologies, confirming checks were dominantly used by the participants. Regarding the mechanisms, three types of mechanisms were found; full two-cycle repair, background check, and short-cut repair sequence.

This study aims to examine EFL learners’ perception toward the application of Orai App in CLL method in teaching speaking. The study employed likert-scale questionnaires to enable researchers to collect massive data. The total of 24 first year students of a private university in East Java – Indonesia, comprising of 5 male and 19 female students participated in this study. Data were analyzed using descriptive statistic by finding out the total score per item. The results of this study illustrated that Orai application helps students alleviate the fear of speaking. It can also help students in increasing their speaking ability on the aspects of pronunciation, intonation as well as the speaking speed. Based on the data analysis, CLL method assisted by Orai application is strongly recommended to be used to assist the students to be better speaker.

Using district level data for urban areas in Java, we reassess the impacts of minimum wages on formal and informal sector employment, unemployment and labour participation. We employ the spatial Du...

Vulnerable dependencies are a known problem in today's free open-source software ecosystems because FOSS libraries are highly interconnected, and developers do not always update their dependencies. Our paper proposes Vuln4Real, the methodology for counting actually vulnerable dependencies, that addresses the over-inflation problem of academic and industrial approaches for reporting vulnerable dependencies in FOSS software, and therefore, caters to the needs of industrial practice for correct allocation of development and audit resources. To understand the industrial impact of a more precise methodology, we considered the 500 most popular FOSS Java libraries used by SAP in its own software. Our analysis included 25767 distinct library instances in Maven. We found that the proposed methodology has visible impacts on both ecosystem view and the individual library developer view of the situation of software dependencies: Vuln4Real significantly reduces the number of false alerts for deployed code (dependencies wrongly flagged as vulnerable), provides meaningful insights on the exposure to third-parties (and hence vulnerabilities) of a library, and automatically predicts when dependency maintenance starts lagging, so it may not receive updates for arising issues.

Running applications on a rooting device makes the application vulnerable to data leakage. Therefore, many applications that require a high level of security are not allowed to run on rooted device. Common technique of detecting rooted device is by using Android API to discover rooting trace. However, the detection can be bypassed using Java function hooking script by the people who want to run the app on rooted device. This research will give illustration that the bypassing process becomes more easy with automation tool and hybrid analysis. In order to create the script, we use combination of static and dynamic analysis with three phases with specific function. Phase 1 aims to detect the estimated Java method that detect rooting, phase 2 will analyze that method on an unrooted device, then phase 3 will create the bypassing script based on the previous result. We also use automation tool to speed up the static analysis. We create two types of script: one that can be used on general application, and the other one that only can be used on specific app. Those types implement different scope: one with the certain Java method, and the other one with specific parameter or return value. In the end, we find that bypassing rooting detection is not complicated if the app use Java function to detect the rooted device. To complicate bypassing process, we encourage the developers to implement more advanced detection rooting technique.

In this paper we examine problems of synchronous collaboration of users in web-based learning environments. It is a strong challenge to develop efficient synchronous groupware systems which provide transparent collaboration of existing applications whereas participants may start at different points in time. Existing collaboration systems either provide transparency or the accommodation of latecomers. We developed a transparent support for accommodating latecomers which may be integrated in any Java-based groupware system on the web.

This article analyses processes of exploitation, accumulation and social reproduction in rural Java. Using fieldwork in a primarily agrarian and a predominantly non-agrarian village, it underlines the ways in which pluri-active labouring class households contribute to processes of accumulation through a variety of forms of petty self-employment and wage-labour–above all sharecropping and female homework, which is interwoven seamlessly with reproductive labour. Agrarian accumulation is dominated by traders and absentee capitalist landowners. Forms of exploitation are linked to petty capitalism's relationship to capital-in-general. The article briefly discusses the potential for labouring class collective action given the villages' relatively flat social structures.

This work develops a distributed environmental monitoring system for the combination of hydroponics and aquaculture based on the internet of things technology, which mainly includes the information perception layer, the information transmission layer and the system architecture. The system has employed multiple sensors terminal to real-time acquisition, including air and water temperatures, dissolved oxygen etc. LoRa protocol is suitable for sending small data and the 4G was employed to collect data and send to the cloud platform. Java is used to develop background applications, to access cloud platforms and local data processing. Based on the collection and processing of environmental data and cloud service platform, the mobile application program client and remote login desktop have been developed. It has been implemented and tested in Tongzhou, Beijing for 3 months in 2020. The results showed the proposed monitoring system stability for overall operation and accuracy data transmission, which can support the actual hydroponics and aquaculture production management. After analysis of monitoring data collected from the developed monitoring system, indoor air and water temperature have the obvious correlation with atmospheric pressure (0.7 and 0.9) and outdoor temperature (1.0 and 0.9), respectively.

Background Sequence-based identification is one of the most effective methods for species-level identification of nontuberculous mycobacteria (NTM). However, it is time-consuming because of the bioinformatics processes involved, including sequence trimming, consensus sequence generation, and public database searches. We developed a simple and fully automated software that enabled species-level identification of NTM from trace files, SnackNTM (https://github.com/Young-gonKim/SnackNTM). Methods JAVA programing language was used for software development. The SnackNTM diagnostic algorithm utilized 16S rRNA gene sequences, according to the Clinical & Laboratory Standards Institute guidelines, and an rpoB gene region was adjunctively utilized to narrow down the species. The software performance was validated using trace files of 234 clinical cases, comprising 217 consecutive cases and 17 additionally selected cases of unique species. Results SnackNTM could analyze multiple cases at once, and all the bioinformatics processes required for sequence-based NTM identification were automatically performed with a single mouse click. SnackNTM successfully identified 95.9% (208/217) of consecutive clinical cases, and the results showed 99.0% (206/208) agreement with manual classification results. SnackNTM successfully identified all 17 cases of unique species. In a processing time comparison test, the analysis and reporting of 30 cases, which took 150 minutes manually, took only 40 minutes with SnackNTM. Conclusions SnackNTM is expected to reduce the workload for NTM identification, especially in clinical laboratories that process large numbers of cases.

When software vulnerabilities threaten the security of users, new research on approaches to reduce security vulnerabilities must be explored. The development of deep learning has opened up the era of automatic code vulnerability detection, extricated humans from multifarious pattern definition, and feature selection. However, existing deep learning based vulnerability detection schemes are still in their early stage, most of them adopted token-level representing schemes, losing the logical information above token level and resulting in the narrowing of differences between codes. They always had low accuracy and high false positive rate. In addition, it is noticed that most code vulnerability detection methods focused on C/C++, and little work can be found on Java. In light of this, we propose an intelligent sentence-level vulnerability self-detection framework (ISVSF), which considers the syntax characteristics of Java and adopts sentence-level method representation and pattern exploration. Experimental results demonstrate that the ISVSF outperforms the existing token-level vulnerability detection schemes in terms of accuracy, false positive rate, detection time, etc. In addition, fast and strong vulnerability feature extraction enables ISVSF to learn vulnerability-related features quickly and achieve high accuracy with providing little training samples, thereby reducing the demand for training dataset effectively.

The purpose of this paper is to examine determinant factors that influence lecturers' productivity during working from home arrangement in the covid-19 pandemic. The focus is the impact of organizational factors that includes IT training, digital infrastructure, and management support, and individual factors as represented by digital orientation on employees' digital capability which in turn affect their productivity. A survey of academic staff from 15 faculties at a state in East Java, Indonesia was conducted as the data collection method. A total of 267 completed questionnaires were analysed using SmartPLS version 2.0. Only an individual's digital orientation was found to have significant impact on the individual's digital capability which in turn affected their d productivity during implementing work from home in this covid-19 pandemic. The research findings suggest the importance of digital orientation in staff selection criteria and for universities to move to greater online delivery of courses combined with flexible working options for staff.

The mobile fog-based cloud (MFBC) plays a major role for the Internet of Things (IoT) and is highly suitable for obtaining virtualized data without additional waiting time. However, the limited battery power of mobile devices limits access to abundant data. To address this problem, an energy-efficient and secure algorithm should be introduced to reduce energy consumption for the MFBC. In this paper, an energy-efficient and secure hybrid (EESH) algorithm is introduced for the MFBC to support the IoT. The EESH algorithm uses the voltage scaling factor to reduce energy consumption. The performance of the EESH algorithm is substantially better than the state-of-the-art contending algorithms when the number of IoT-tasks increases, and EESH consumes minimal energy. Furthermore, the identity of mobile cloud users is of paramount significance; therefore, the EESH is further secured by applying a malicious data detection (MDD) algorithm using blockchain technology. The capability of the processors on the server side of the MFBC is analyzed and improved for the IoT. The proposed EESH, MDD and underlying algorithms are programmed on the Java platform. Finally, the performance of the proposed algorithms on the MFBC is compared with that of known algorithms in terms of security, energy efficiency, throughput and latency.

Automated program repair is a promising approach to reducing the costs of manual debugging and increasing software quality. However, recent studies have shown that automated program repair techniques can be prone to producing patches of low quality, overfitting to the set of tests provided to the repair technique, and failing to generalize to the intended specification. This paper rigorously explores this phenomenon on real-world Java programs, analyzing the effectiveness of four well-known repair techniques, GenProg, Par, SimFix, and TrpAutoRepair, on defects made by the projects' developers during their regular development process. We find that: (1) When applied to real-world Java code, automated program repair techniques produce patches for between 10.6% and 19.0% of the defects, which is less frequent than when applied to C code. (2) The produced patches often overfit to the provided test suite, with only between 13.8% and 46.1% of the patches passing an independent set of tests. (3) Test suite size has an extremely small but significant effect on the quality of the patches, with larger test suites producing higher-quality patches, though, surprisingly, higher-coverage test suites correlate with lower-quality patches. (4) The number of tests that a buggy program fails has a small but statistically significant positive effect on the quality of the produced patches. (5) Test suite provenance, whether the test suite is written by a human or automatically generated, has a significant effect on the quality of the patches, with developer-written tests typically producing higher-quality patches. And (6) the patches exhibit insufficient diversity to improve quality through some method of combining multiple patches. We develop JaRFly, an open-source framework for implementing techniques for automatic search-based improvement of Java programs. Our study uses JaRFly to faithfully reimplement GenProg and TrpAutoRepair to work on Java code, and makes the first public release of an implementation of Par. Unlike prior work, our study carefully controls for confounding factors and produces a methodology, as well as a dataset of automatically-generated test suites, for objectively evaluating the quality of Java repair techniques on real-world defects.

Realizing an effective earthquake early warning system (EEWS) in the case of extensive regions and noisy signals is challenging, particularly in East Java, Indonesia. This letter proposes the rapid detection of the p-wave arrival and determination of the earthquake's hypocenter and magnitude using deep learning. The Ncheck algorithm is used for noise handling for picking the p-arrival on a multistation waveform as a form of picking target window prediction (PTWP). Then, multitarget regression (MTR) with a hard-shared orthogonal optimization model is proposed for earthquake parameter determination. The data sets used contained data of earthquakes recorded at three stations from the Indonesian seismic network in East Java; 2009-2017 data were used for training and validation, and 2019 data were used for real-time testing. The results show that the PTWP for picking p-arrival has a mean absolute error (MAE) of 0.12 s, and the MTR for earthquake magnitude, longitude, latitude, depth, and origin time detection shows MAEs of 0.21 M, 9.44, 18.72, 27.81 km, and 2.78 s, respectively.

Ring confidential transaction (RingCT) protocol is widely used in cryptocurrency to protect the privacy of both users' identities and transaction amounts. Most recently, a new RingCT protocol was proposed by leveraging cryptographic accumulators, which can achieve a constant-size output theoretically but still far from being practical due to the heavy zero-knowledge associated with the accumulator. In this work, we revisit the design of ring confidential transaction protocol and put forward a more efficient privacy-preserving payment protocol, which is built upon a extended version of one-out-of-many proof and a special multi-signature. Compared with previous works, the new protocol is not only more practical, but also does not suffer from a trusted setup. Besides, we show that the protocol satisfies the security requirements provided that the underlying cryptographic primitives are secure in the random oracle model. We implement our new payment protocol in Java, and the experimental results show that it is efficient enough to be used in practice.

System services and resources in the Android are accessed through IPC based mechanisms. However, previous research has demonstrated that they are vulnerable to the denial-of-service attack (DoS attack). For instance, the JNI global reference (JGR), which is widely used by system services, can be exhausted and cause system reboot (hence the name JGRE attack). Even though the Android team tries to fix the problem by enforcing the security check, however, we find that it is still possible to construct a JGR exhaustion DoS attack in Android. In this paper, we propose a new JGR exhaustion DoS attack, which is effective in different Android releases, including the latest one (i.e., Android 10). Specifically, we developed JGREAnalyzer, a tool that can systematically detect JGR vulnerable services APIs via callgraph analysis and forwarding reachability analysis. We applied this tool to different Android versions and found multiple vulnerabilities. In particular, among 148 system services in Android 10, 12 system services have 21 vulnerabilities. Among them, 9 can be successfully exploited without any permission. We further analyze the root cause of the vulnerabilities and propose a new defense to throttle the JGRE attack by restricting resource consumption via global reference counting.

Model-driven methodology has been widely adopted in embedded software design, and Dataflow is a widely used computation model, with strong modeling and simulation ability supported in tools such as Ptolemy. However, its code synthesis support is quite limited, which restricts its applications in real industrial practice. In this paper, we focus on the automatic code synthesis of Dataflow, and implement , a code generator that could support most of the widely used modeling features such as expression type and boolean switch, more efficiently. First, we disassemble the Dataflow model into actors embedded in if-else or switch-case statements based on schedule analysis, which bridges the semantic gap between the code and the original Dataflow model. Then, we design well-designed templates for each actor, and synthesize well-structured executable C and Java codes with sequential code assembly. Compared to the existing C and Java code generators of Dataflow model in Ptolemy-II, and the C code generator in Simulink, the lines of code synthesized by are decreased by an average of , and , and the execution time of the synthesized code by is also decreased by an average of , and respectively.

Religious culture in the village of Temboro, which made it is known as "Kampung Madinah", certainly did not happen suddenly, but there was a process of formation and role of Kiai and Pesantren Al-Fatah. This study aims to analyze the Kiai’s cultural strategy in developing a religious culture using a phenomenological approach and collects data through in-depth interviews, active participant observation, and documentation. The study results show that, firstly, ideological culture is the strategy of the Kiai in shaping the religious culture of the Temboro community. Secondly, the ideological culture of the Tablighi Jamaat has an impact on changing the five religious cultures of the Temboro community, namely: (1) ideological faith, (2) normative Islam, (3) Sufistic religious experience, (4) moderate-textual religious knowledge, (5) Maqami-intiqali practice.

In Indonesia, ground-based rainfall monitoring is uneven and sometimes lacks continuity especially in small watersheds, which makes hydrological modeling difficult. This paper aims to the performance evaluation of the HBV Light model from the manual measurement of rainfall (MMR), Global Precipitation Measurement (GPM-3IMERGDF), and Tropical Rainfall Measuring Mission (TRMM-3B42) as input for the hydrological model. The Hydrologiska Byrans Vattenbalansavdelning (HBV) Light hydrological model is applied to three small watersheds, namely Sampean Baru, Bedadung, and Mayang. The model's performance evaluation is assessed based on the correlation between the average rainfall data for the satellite product area and the MMR product, the stationarity of the rainfall and discharge data, and the model accuracy. The model simulation results show that the MMR rainfall in all watersheds provides a better discharge response than the other two products. Meanwhile, the simulation model of the GPM-3IMERGDF satellite product is slightly better than TRMM-3B42. The stationarity test of rainfall and discharge data needs to be enforced before modeling.

Identifying refactoring operations in source code changes is valuable to understand software evolution. Therefore, several tools have been proposed to automatically detect refactorings applied in a system by comparing source code between revisions. The availability of such infrastructure has enabled researchers to study refactoring practice in large scale, leading to important advances on refactoring knowledge. However, although a plethora of programming languages are used in practice, the vast majority of existing studies are restricted to the Java language due to limitations of the underlying tools. This fact poses an important threat to external validity. Thus, to overcome such limitation, in this paper we propose RefDiff 2.0, a multi-language refactoring detection tool. Our approach leverages techniques proposed in our previous work and introduces a novel refactoring detection algorithm that relies on the Code Structure Tree (CST), a simple yet powerful representation of the source code that abstracts away the specificities of particular programming languages. Despite its language-agnostic design, our evaluation shows that RefDiff's precision (96%) and recall (80%) are on par with state-of-the-art refactoring detection approaches specialized in the Java language. Our modular architecture also enables one to seamless extend RefDiff to support other languages via a plugin system. As a proof of this, we implemented plugins to support two other popular programming languages: JavaScript and C. Our evaluation in these languages reveals that precision and recall ranges from 88% to 91%. With these results, we envision RefDiff as a viable alternative for breaking the single-language barrier in refactoring research and in practical applications of refactoring detection.

Most techniques for automated program repair (APR) use tests to drive the repair process; this makes them prone to generating spurious repairs that overfit the available tests unless additional information about expected program behavior is available. Our previous work on JAID, an APR technique for Java programs, showed that constructing detailed state abstractions-similar to those employed by techniques for programs with contracts-from plain Java code without any special annotations provides valuable additional information, and hence helps mitigate the overfitting problem. This paper extends the work on JAID with a comprehensive experimental evaluation involving 693 bugs in three different benchmark suites. The evaluation shows, among other things, that: 1) JAID is effective: it produced correct fixes for over 15% of all bugs, with a precision of nearly 60%; 2) JAID is reasonably efficient: on average, it took less than 30 minutes to output a correct fix; 3) JAID is competitive with the state of the art, as it fixed more bugs than any other technique, and 11 bugs that no other tool can fix; 4) JAID is robust: its heuristics are complementary and their effectiveness does not depend on the fine-tuning of parameters. The experimental results also indicate the main trade-offs involved in designing an APR technique based on tests, as well as possible directions for further progress in this line of work.

The purpose of the study was to describe the implementation of Javanese cultural education in elementary schools in Yogyakarta. The research method is a qualitative descriptive approach. Data collection techniques used field observation techniques, in-depth interviews, and documentation studies. Analysis of the data that has been collected is reduced and presented. Conclusions on the implementation of Javanese education and culture are as follows: (1). Implementation of the school's vision, mission and goals (2). Curriculum development in Javanese cultural education (3). Javanese cultural education learning (4). Habituation in Javanese cultural educators. Supporting factors are (a) good school background (b). Local government regulations support (c). Complete facilities and infrastructure (d) support from all school components (e). Good school management 6). Students come from Javanese society. Inhibiting factor (a). There are no expert instructors (b). There are no standard guidelines (c). Student interest is changing, (d). Lack of teachers in developing Javanese cultural education (e). Not maximal in utilizing learning media.

Traditional medicines are commonly used in the community. One type of traditional medicines that is popular among people is "jamu". "Jamu" can be made from plants, animals, minerals, or a mixture of them that has been used for generations based on empiric experience. This study aimed to describe perceptions of the use of traditional medicines for self-medication among people in Dieng Plateau, Kejajar, Wonosobo, Central Java Province. This is a descriptive study with a qualitative approach. Respondents were adults more than 18 years old who had experience in using traditional medicines and were selected with non-random accidental sampling. Thirty-one (31) respondents signed informed consent. Data were collected using an interview guideline and analyzed using enumerative approach. Most of the respondents used traditional medicines for self-medication one to five times a month. They expressed that traditional medicine is like "Jamu", primarily liquid, with limited or no side effects. Traditional medicine was mostly obtained from local groceries at affordable prices. Most of the respondents have a positive attitude towards the use of traditional medicines. They have the willingness to use traditional medicines to deal with their symptoms or minor illnesses. Their satisfaction experience in using traditional medicine will motivate them to do so in the future. Based on the results, it can be said that traditional medicines have an important role in the health care system particularly for self-medication.

This study aims to examine and analyze the effect of Literacy Rate (LR) and Gross Regional Domestic Product (GRDP) on Poverty in the Big Cities of Java Island from 2015 to 2019. The population in this study is all people in the poor category in Indonesia. The samples in this study were people who were categorized as poor in the big cities of Java, namely the Special Capital Region of Jakarta, Bandung, Semarang, Surabaya, DIY, and Banten. Data were analyzed using panel data regression (pooled data) with a total sample of 30 data. The data processing tools in this study use Ms. Excel and Eviews no 9. The panel data regression model options include Estimating Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM). Model selection using Chow Test and Hausmant Test. The results of the two tests recommend researchers use the output of the Fixed Effects Model for hypothesis testing. Results show that the Literacy Rate (LR) has a negative and significant effect on poverty in Big Cities in Java. GDRP doesn’t affect poverty in Big Cities in Java

This research is an explanatory research by applying the survey method, in which proposed to examine the effect of integrated marketing communication and service quality on purchase decisions mediated by consumer satisfaction at Maybank Finance, Java Area, Non Jabodetabek in the Covid-19 Pandemic Era. The sample of this research is 100 consumers who are new debtors who will do financing at Maybank Finance in the Java Region of the Non-jabodetabek area. Data obtained through questionnaires, that was spreaded to respondents who were selected by purposive sampling method. The analytical technique used in this study is the Structural Equation Model (SEM) using the SMARTPls 3 program. The results reveal that the direct influence of integrated marketing communication and service quality on consumer purchase decisions is no significant. The direct effect of consumer satisfaction on consumer purchase decisions is positive and significant, in which the path coefficient of 0.536 with a p value of 0.000. The results of the mediation effect test show that consumer satisfaction does not play a role in mediating the effect of integrated marketing communications on consumer purchase decisions. Consumer satisfaction plays a role in mediating the effect of service quality on purchase decisions.

This research aims to identify the planning implementation, idenfety Management System (SMK3) at the workshop of vocational high schools in the major of construction and property engineeringtify the implementation, and identify the evaluation of the implementation of the Occupational Health And Safety Management System (SMK3) at the workshop of Construction and Property Engineering Study Program, one of the Vocational High Schools in Central Java. This research is qualitative by using data collection techniques in interviews, observations, and documentation the validity of the data using source triangulation. Data analysis used descriptive qualitative. The results show that the workshop building at one of the Vocational High Schools in Central Java has been well planned in its implementation of OHS. The implementation of OHS is categorized as quite well implemented. The evaluation of the implementation of OHS is categorized as quite well done. Hence, the Occupational Health and Safety (OHS) management system at the Vocational High Schools needs to be improved. The school can collaborate with stakeholders to follow up on the management system of OHS.

Purpose This study aims to prove that land-use change plays a role in the occurrence of hydro-meteorological disasters in Central Java, especially in relation to its upstream and downstream. Design/methodology/approach The paper presents empirical findings from quantitative research using a spatial analysis and descriptive analysis. Findings The upstream and downstream area of Central Java is categorized as a rapid development area that results in changes in land use and land cover. The findings showed that there was an increasing number of hydrometeorological disasters such as floods and landslides as the impact of land-use change and rainfall conditions. Research limitations/implications Analysis of the relationship between rainfall and disaster events with more technical and specific analysis could be done in the further research. Originality/value In this study, more analysis in the context of river basin systems including upstream and downstream in different periods to examine the linkage between them have been considered and incorporated.

This study aims to determine the factors that influence educated unemployment in Java. The data used in this study is secondary data using quantitative methods. Data analysis uses panel data analysis which is a combination of time series and cross-section data. The time-series data uses data for the 2015-2019 period and cross-section data from 6 provinces on the island of Java. The results showed that simultaneously all variables had a significant effect on the level of educated unemployment. While partially shows that the variable level of education and PMDN have a significant positive impact on educated unemployment, and the UMR variable has a significant negative impact on educated unemployment.

Coronavirus is a big family of viruses that causes acute respiratory syndrome and mediates human-to-human by the environment. A factor that affects the spread of infectious diseases is population density. Therefore, it is necessary to study the effect of population density on infectious diseases like COVID-19. The research analyzed the effect of the population density of each regency in East Java on cumulative confirmed COVID-19 cases until December 9, 2020. The research applied quantitative method using the agglomerative hierarchical clustering method. The clustering method included single, average, and complete linkages. The results of clustering using single linkage and average linkages have the same results for the population density of Jember Regency. This regency has the lowest effect for the cumulative confirmed COVID-19 cases. Then, complete linkage obtains that Banyuwangi Regency and Gresik Regency has the population density with the lowest effect for the cumulative number of confirmed COVID-19 cases. The results of clustering with single, average, and complete linkages have the same results for population density with a big effect on the cumulative number of confirmed COVID-19 cases in Surabaya City. The results of best clustering regencies or cities that population density affects the number of confirmed cases of COVID-19 use a single linkage.

This study aims to determine the interest of Islamic hospitals in East Java in choosing the sharia sofware application “SI AISAH” from the Sharia Accounting Information System Service Centre (PLJSIAS) University of Muhammadiyah Surabaya. This research is a quantitative research using Statistical Package for the Social Sciences Software IBM SPSS version 23. The variables used ini this study are: Sharia, User Safety, Prices, and User Friendly. The population in this study were the leader and employees of Islamic hospital in East Java who were involved with the use of this sofware. Sampling using random sampling tehnique, with a total sample of 105 respondents. The results of the analysis show that the factors that influence the decision of Islamic hospitals in East Java to choose the “SI AISAH” software applicatin from PLJSIAS UMSurabaya are: Sharia (X1), User Safety (X2), Prices (X3), and User Friendly (X4). These four factors influence the decision of Islamic hospitals in East Java in choosing the “SI AISAH” software application only 69,9%, while the remaining 30,1% in influenced by other factor, and the most significants factor is the sharia factor (X1).

Software maintenance and evolution can introduce defects in software systems. For this reason, there is a great interest to identify defect prediction and estimation techniques. Recent research proposes just-in-time techniques to predict defective changes just at the commit level allowing the developers to fix the defect when it is introduced. However, the performance of existing just-in-time defect prediction models still requires to be improved. This paper proposes a new approach based on a large feature set containing product and process software metrics extracted from commits of software projects along with their evolution. The approach also introduces a deep temporal convolutional networks variant based on hierarchical attention layers to perform the fault prediction. The proposed approach is evaluated on a large dataset, composed of data gathered from six Java open-source systems. The obtained results show the effectiveness of the proposed approach in timely predicting defect proneness of code components.

During the pandemic, Sunday School activities have challenges: Sunday school teachers are not familiar with technology, children are not used to technology and online meetings. This research was conducted by simple random sampling in several churches in East Java in the Middle class. The total population is 60 children, and the sample taken is 52 children. Data analysis measures the relationship between variables using a correlation test and the magnitude of the influence between variables. This study shows that a teacher can start the Sunday school activities with asynchronous learning during the pandemic using YouTube and WhatsApp media combined with engaged learning strategies that can make children feel involved.

Gender is a multidimensional issue that's not limited to gender discrimination , but also includes the economic, educational, and health aspects, which then become the focus of almost all the Sustainable Development Goals (SDGs). Evaluation of the development devoted to the perspective of the gender using several indicators, Gender Development Index (GDI) and Gender Empowerment Measure (GEM). GEM describes the role of women in the economic sphere and is measured by equality in political participation. GEM of East Java for 5 consecutive years (2014 – 2018) is lower than the average national GEM. This study aims to identify factors affecting GEM in East Java using nonparametric regression spline quadratic. The result of the regression model shows the factors affecting GEM East Java is the L abor F orce P articipation R ate ( LFPR ) population of women ( ), School Participation Rate ( SPR ) high school population of women ( ), Percentage of Population Female that W orking in the formal sector ( ), sex ratio ( ), Percentage of Population Female that Working as members of People’s Representative Council ( ), Percentage of Population Female that working as C ivil S ervants ( ), and rate of women's income donations ( ). The model generates v alue of 93.74% and MAPE of 3.22%. This research contributes to the implementation of non-parametric spline regression in identifying various factors that influence social phenomena.

Tobacco (Nicotiana tabacum L.) is a plant used as a mixture of cigarettes, and recreational media especially for men. This study aimed to identify variations, and determine the relationship between tobacco cultivars in Central Java and Yogyakarta based on macromorphological and micromorphological characters. Sampling locations are determined by surveying locations in both regions. Tobacco samples found include 5 cultivars in Central Java namely 'Mantili', 'Uler Magetan', 'Garut', ‘Gober Boyolali’, 'Manila', and 3 cultivars in Yogyakarta namely 'Siluk', 'Java', and 'Virginia'. Characterization with 23 qualitative macromorphological characters including leaves, and stems, with 9 qualitative and quantitative micromorphological characters including trichome and stomata. Descriptive data analysis is done to obtain the typical character of each cultivar, followed by numerical analysis including scoring characters processed with MVSP (Multi Variate Statistical Package), clustering with UPGMA (Unweighted Pair Group Method with Averages), and calculation of similarity coefficients with Simple matching formula. The results showed variations in the macromorphological characters including the shape of the leaf lamina, the base of the leaf, the absence of leaf stalks, and type of leaf venation. Tobacco has anisositic stomata, and varies in terms of length, width, and density of stomata. Tobacco trichomes are glandular. The result dendrograms form two clusters (A and B) with the similarity index of each cluster above 0.80. Cultivars with close relationships such as 'Siluk'-'Java', and far relationship like 'Java'-'Manila'.

Filariasis limfatik merupakan salah satu penyakit menular yang disebabkan oleh cacing filaria dan ditularkan melalui berbagai jenis nyamuk, penyakit ini masih menjadi masalah kesehatan di Indonesia. Dampak kesehatan dari penyakit filariasis adalah rasa sakit, kecacatan serta menghambat produktivitas. Berdasarkan profil Kesehatan Kabupaten Brebes 2019 diketahui bahwa terdapat 61 kasus filariasis. Kasus filariasis tertinggi ditemukan pada Kecamatan Ketanggungan, Paguyangan dan Bantarkawung. Tujuan penelitian ini untuk mengetahui keragaman spesies nyamuk yang diduga menjadi vektor filariasis di tiga kecamatan Kabupaten Brebes. Jenis penelitian ini menggunakan metode deskriptif dengan pengambilan sampel secara purposive. Pengumpulan data dilakukan dengan survei entomologi. Data dianalasis secara deskriptif berdasarkan kelimpahan nisbi, frekuensi dominansi nyamuk dan keberadaan mikrofilaria pada nyamuk. Hasil penelitian menemukan enam spesies yaitu Culex quinquefasciatus, Cx. vishnui, Cx. tritaeniorhyncus, Cx. bitaeniorhyncus, Armigeres sp dan Aedes aegypti. Spesies nyamuk yang dominan di tiga kecamatan tersebut adalah Culex quinquefasciatus, dengan angka dominansi sebesar 73,04 di Kecamatan Ketanggungan, 50 di Kecamatan Bantarkawung dan 43,75 di Kecamatan Paguyangan. Kondisi lingkungan fisik suhu dan lingkungan di lokasi tersebut yaitu 19-31,7oC dan 59-99%. Simpulan penelitian ini yaitu nyamuk yang berpotensi sebagai vektor filariasis adalah Culex quinquefasciatus dengan lokasi terbanyak di Kecamatan Ketanggungan.