Reviewerly/exHarmony huggingface dataset:
Investigating the factors which impact SPI implementation initiatives in software SMEs—A systematic map and review

Abstract Context For successful initiation of software process improvement (SPI) initiatives in software SMEs, it is vital for the practitioners to have deep understanding of various SPI inhibitors. Objective The paper has the following objectives: investigate factors which impact SPI implementation initiatives in software SMEs, synthesize available evidence, identify gaps between existing approaches available in the literature, and to develop the taxonomy of the factors. Methods Database search and snowballing procedure are adapted to perform the systematic mapping and review, which includes 105 primary studies published between 2002 and 2018. Also, we reviewed the empirical studies for their rigor and relevance. Results A total of 16 inhibitors have been identified and are grouped into PMBOK knowledge areas framework. From the rigor and relevance scores, it is observed that despite the research has been conducted in an industrial context, it lacks rigor (mean = 1.79) and thus hinders the possibility of generalizing the results. This calls for more studies with thorough empirical analysis and validation using adequate statistical tests. Conclusions The results can help the practitioners to understand the dynamics of factors, which undermine SPI implementation in software SMEs and also to assist them in the development of effective control and mitigation strategies.
A Computational Approach to Author Identification from Bengali Song Lyrics

Music is one of the truest forms of art. People listen to music both as a form of entertainment and means of relaxation. Every country or region in the world has its own form and style of music. Bangladesh is no exception as it has a great history of music with a great tradition of song writings over centuries. Although songs are very popular among the enthusiasts, authors of them get little recognition. As a result, author identification from songs, more specifically from lyrics, is an important and realistic possibility. Authorship attribution is one of the ways of identifying the author from a linguistic corpus. This paper demonstrates a guideline to identify the author of a Bengali song from the lyrics of that song using machine learning. It presents the first work on machine learning-based computational approach for author attribution from the lyrics of Bengali songs. Six methods of machine learning were used for the author identification, and high accuracy had been achieved from these methods while applied to the data sets D2A, D4A, and D7A, which were built from Bengali song lyrics. It is observed that the Naive Bayes (NB) classifier provides higher accuracy in comparison with the other methods as it shows 93.9, 85, and 86.7% of accuracy while considering the stop words for our three data sets, respectively.
Accelerating Density Peak Clustering Algorithm

The Density Peak Clustering (DPC) algorithm is a new density-based clustering method. It spends most of its execution time on calculating the local density and the separation distance for each data point in a dataset. The purpose of this study is to accelerate its computation. On average, the DPC algorithm scans half of the dataset to calculate the separation distance of each data point. We propose an approach to calculate the separation distance of a data point by scanning only the neighbors of the data point. Additionally, the purpose of the separation distance is to assist in choosing the density peaks, which are the data points with both high local density and high separation distance. We propose an approach to identify non-peak data points at an early stage to avoid calculating their separation distances. Our experimental results show that most of the data points in a dataset can benefit from the proposed approaches to accelerate the DPC algorithm.
Informing the Design of Conversational IR Systems

Recent developments in conversational IR have raised questions about the nature of interactions which occur between the user and the system and the cognitive capabilities expected of such systems. In our research, we investigate the completeness of existing theoretical frameworks in explaining conversational search data propose modifications to such systems. The linear and transient nature of speech makes it cognitively challenging for the user to process a large amount of information. We propose a study to evaluate the users' preference of modalities when using conversational search systems. The study will help us to understand how results should be presented in a conversational search system. As we observe how users search using audio queries, interact with the intermediary, and process the results presented, we aim to develop an insight on how to present results more efficiently in a conversational search setting. We also plan on exploring the effectiveness and consistency of different media in a conversational search setting. Our observations will inform future designs and help to create a better understanding of such systems.
Towards Mining Answer Edits to Extract Evolution Patterns in Stack Overflow

The current state of practice dictates that in order to solve a problem encountered when building software, developers ask for help in online platforms, such as Stack Overflow. In this context of collaboration, answers to question posts often undergo several edits to provide the best solution to the problem stated. In this work, we explore the potential of mining Stack Overflow answer edits to extract common patterns when answering a post. In particular, we design a similarity scheme that takes into account the text and code of answer edits and cluster edits according to their semantics. Upon applying our methodology, we provide frequent edit patterns and indicate how they could be used to answer future research questions. Assessing our approach indicates that it can be effective for identifying commonly applied edits, thus illustrating the transformation path from the initial answer to the optimal solution.
Towards a Knowledge Warehouse and Expert System for the Automation of SDLC Tasks

Cost of a skilled and competent software developer is high, and it is desirable to minimize dependency on such costly human resources. One of the ways to minimize such costs is via automation of various software development tasks. Recent advances in Artificial Intelligence (AI) and the availability of a large volume of knowledge bearing data at various software development related venues present a ripe opportunity for building tools that can automate software development tasks. For instance, there is significant latent knowledge present in raw or unstructured data associated with items such as source files, code commit logs, defect reports, comments, and so on, available in the Open Source Software (OSS) repositories. We aim to leverage such knowledge-bearing data, the latest advances in AI and hardware to create knowledge warehouses and expert systems for the software development domain. Such tools can help in building applications for performing various software development tasks such as defect prediction, effort estimation, code review, etc.
A Combined Micro-block Chain Truncation Attack on Bitcoin-NG

Bitcoin-NG, introduced by Eyal et al. in NSDI 2016, divides a blockchain into key-blocks and micro-blocks to improve transaction process efficiency. In this paper, we propose a novel attack on Bitcoin-NG, called a micro-block chain truncation attack. Combined with key-block selfish and stubborn mining, and an eclipse attack, this attack is able to bring extra reward to attackers in Bitcoin-NG than in Bitcoin through a colluded strategy or a “destroyed if no stake” strategy. Our evaluation calculates the reward proportion of an attacker by these attacks on both Bitcoin-NG and Bitcoin, which helps us figure out whether Bitcoin-NG sacrifices security for efficiency promotion. We also evaluate 18 strategy combinations by traversing different values of computation power and practical truncation rate. In a setting with reasonable parameters, the optimal combined micro-block chain truncation attack obtains at most 5.28% more reward proportion in Bitcoin-NG than in Bitcoin.
Decision-Making Support Method Based on Sentiment Analysis of Objects and Binary Decision Tree Mining

As more and more users express their opinions on many topics on Twitter, the sentiments contained in these opinions are becoming a valuable source of data for politicians, researchers, producers, and celebrities. These sentiments significantly affect the decision-making process for users when they assess policies, plan events, design products, etc. Therefore, users need a method that can aid them in making decisions based on the sentiments contained in tweets. Many studies have attempted to address this problem with a variety of methods. However, these methods have not mined the level of users’ satisfaction with objects related to specific topics, nor have they analyzed the level of users’ satisfaction with that topic as a whole. This paper proposes a decision-making support method to deal with the aforementioned limitations by combining object sentiment analysis with data mining on a binary decision tree. The results prove the efficacy of the proposed approach in terms of the error ratio and received information.
A Data-Driven Security Game to Facilitate Information Security Education

Many universities have started to educate students on how to develop secure software and systems. One challenge of teaching information security is that the curriculum can easily be outdated, because new attacks and mitigation approaches arise. It is therefore necessary to provide software developers with methods and tools that are attractive (e.g., computer games) for self-study and up-to-date information security knowledge during and after the university education. This paper presents an on-going study to develop an educational game to facilitate information security education. The game is developed as a single player Tower Defense (TD) game. The educational goal of the game is to teach developers, who are not security experts, how to choose proper mitigation strategies and patterns to defend against various security attack scenarios. One key benefit of our game is that it is data driven, meaning, it can continuously fetch data from relevant security-based online sources (e.g., Common Attack Pattern Enumeration Classification CAPEC) to stay up to date with any new information. This is done automatically. We evaluated the game by letting students play it and give comments. Evaluation results show that the game can facilitate students learning of mitigation strategies to defend against attack scenarios.
Design and Research on the Chinese Medicine Health Management System Based on the Wireless Sensor Network

In view of the rapid growth of China’s aging population and the rising demand for the medical services, it is more necessary to build a health management system of the traditional Chinese medicine based on the wireless sensor networks. This design combines the ZigBee technology, the city grid management and the virtual reality technology, to establish a real-time, accurate and fine health monitoring system, so as to comprehensively improve the efficiency, quality and level of people’s health management.
Blockchain Network Threats: The Case of PoW and Ethereum

Developers do not properly take into account possible problems on the Internet. This fact can cause problems in the operation of blockchain systems. The subject of our analysis was Ethereum due to its short block creation time and, as a result, a strong reaction to faults in the network. We checked the possible threats from the miners during the unreliable work of the Internet. For the study, we conducted many simulations of the work of Ethereum with different delays in the propagation of blocks and various mining power allocations. The results showed that if the duration of the troubles is more than 10 h, a miner with 26% can capture the entire Ethereum network, but this requires the intervention of people controlling other nodes. This is possible because it will be more cost-effective for them.
PYE

Languages like Java and C# follow a two-step process of compilation: static compilation and just-in-time (JIT) compilation. As the time spent in JIT compilation gets added to the execution-time of the application, JIT compilers typically sacrifice the precision of program analyses for efficiency. The alternative of performing the analysis for the whole program statically ignores the analysis of libraries (available only at runtime), and thereby generates imprecise results. To address these issues, in this article, we propose a two-step (static+JIT) analysis framework called precise-yet-efficient (PYE) that helps generate precise analysis-results at runtime at a very low cost. PYE achieves the twin objectives of precision and performance during JIT compilation by using a two-pronged approach: (i) It performs expensive analyses during static compilation, while accounting for the unavailability of the runtime libraries by generating partial results, in terms of conditional values , for the input application. (ii) During JIT compilation, PYE resolves the conditions associated with these values, using the pre-computed conditional values for the libraries, to generate the final results. We have implemented the static and the runtime components of PYE in the Soot optimization framework and the OpenJDK HotSpot Server Compiler (C2), respectively. We demonstrate the usability of PYE by instantiating it to perform two context-, flow-, and field-sensitive heap-based analyses: (i) points-to analysis for null-dereference-check elimination; and (ii) escape analysis for synchronization elimination. We evaluate these instantiations against their corresponding state-of-the-art implementations in C2 over a wide range of benchmarks. The extensive evaluation results show that our strategy works quite well and fulfills both the promises it makes: enhanced precision while maintaining efficiency during JIT compilation.
Generating Synthetic Graphs for Large Sensitive and Correlated Social Networks

With the fast development of social networks, there exists a huge amount of users information as well as their social ties. Such information generally contains sensitive and correlated users' personal data. How to accurately analyze this large and correlated social graph data while protecting users' privacy has become a challenging research issue. In the literature, various research efforts have been made on this topic. Unfortunately, correlation based privacy protection techniques for social graph data have seldom been investigated. To the best of our knowledge, this paper is the first attempt to resolve this research issue. Particularly, this paper first protects users' raw data via local differential privacy technique and then a correlation based privacy protection approach is designed. Last, a K-means algorithm is applied on the perturbed local data and the clustering results are used to generate the synthetic graphs for further data analytics. Experiments are evaluated on two real-world data sets, i.e. Facebook dataset and Enron dataset, and the promising experimental results demonstrate that the proposed approach is superior to the state-of-the-art LDPGen and the baseline method, e.g. the DGG, with respect to the accuracy and utility evaluation criteria.
On-item fixations during serial encoding do not affect spatial working memory

Ample evidence suggests that there is overlap between the eye-movement system and spatial working memory. Such overlapping structures or capacities may result in interference on the one hand and beneficial support on the other. We investigated eye-movement control during encoding of verbal or spatial information, keeping the display the same between tasks. Saccades to to-be-encoded items were scarce during spatial encoding in comparison with verbal encoding. However, despite replicating this difference across different tasks (serial, free recall) and presentation modalities (simultaneous, sequential presentation), we found no relation between item fixations and memory performance—that is, no costs or benefits. Inducing a change from covert to overt encoding did not affect spatial memory performance as well. In contrast, regressive fixations on prior items, that were no longer on the screen, were associated with increased spatial memory performance. Regressions occurred mainly at the end of the encoding period and were targeted at the first presented item. Our results suggest a dissociation between two types of fixations that accompany serial spatial memory: On-item fixations are epiphenomenal; regressions indicate rehearsal or output preparation.
Triangle Counting on GPU Using Fine-Grained Task Distribution

Due to the irregularity of graph data, designing an efficient GPU-based graph algorithm is always a challenging task. Inefficient memory access and work imbalance often limit GPU-based graph computing, even though GPU provides a massively parallelism computing fashion. To address that, in this paper, we propose a fine-grained task distribution strategy for triangle counting task. Extensive experiments and theoretical analysis confirm the superiority of our algorithm over both large real and synthetic graph datasets.
Soft computing decision making system to analyze the risk factors of T2DM

Type-2 Diabetes mellitus is one of the most alarming diseases in both developed and developing countries. The WHO predicted that 90% of people around the globe will suffer from T2DM (WHO, 2016). Most of the people are living in India without knowing that they are affected with T2DM. So, the undiagnosed T2DM leads to the complication in heart, kidney disease, eye and feet. Even though Type 2 diabetes has many risk factors associated to it, lifestyle changes play a vital role in triggering the Type 2 diabetes. Hence, the objective of the present study is to analyze and identify the most influencing risk factors of T2DM. Determining the most influencing risk factor of T2DM is not an easy task as there are lot of complexity and uncertainty involved in it. To tackle this issue, a novel decision making system is designed by combining the salient features of The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Fuzzy Cognitive Map (FCM). Eight risk factors are chosen as the input variables for the system. The proposed system elucidates that Blindness, Obesity, Physical Inactivity are the most influencing factors for the type 2 diabetes mellitus.
Towards Unified Software Project Monitoring for Organizations using Hybrid Processes and Tools

Large-scale software development organizations generally carry out multiple software development projects simultaneously. Teams use various software development processes and tools to implement these projects. In this context, the main challenges of the practitioners are (1) keeping track of the status of a single project where hybrid set of tools exist for different software life cycle activities (2) effectively monitoring a consolidated status of multiple projects that use hybrid processes and tools. To address these challenges, it is vital to have a unified view of these projects independent from these hybrid processes and tools. To this end, we report on our preliminary experiences on the development of a unified project monitoring solution and a corresponding tool support based on the Essence framework's language and kernel. Our solution provides an up-to-date and unified view of projects by collecting data from various tools automatically as well as allowing manual data entry.
Active Two Phase Collaborative Representation Classifier

The Sparse Representation Classifier, the Collaborative Representation Classifier (CRC), and the Two Phase Test Sample Sparse Representation (TPTSSR) classifier were introduced in recent times. All these frameworks are supervised and passive in the sense that they cannot benefit from unlabeled data samples. In this paper, inspired by active learning paradigms, we introduce an active CRC that can be used by these frameworks. More precisely, we are interested in the TPTSSR framework due to its good performance and its reasonable computational cost. Our proposed Active Two Phase Collaborative Representation Classifier (ATPCRC) starts by predicting the label of the available unlabeled samples. At testing stage, two coding processes are carried out separately on the set of originally labeled samples and the whole set (original and predicted label). The two types of class-wise reconstruction errors are blended in order to decide the class of any test image. Experiments conducted on four public image datasets show that the proposed ATPCRC can outperform the classic TPTSSR as well as many state-of-the-art methods that exploit label and unlabeled data samples.
TWINS - This Workflow Is Not Scrum: Agile Process Adaptation for Open Source Software Projects

It is becoming commonplace for companies to contribute to open source software (OSS) projects. At the same time, many software organizations are applying Scrum software development practices, for productivity and quality gains. Scrum calls for self-organizing teams, in which the development team has total control over its development process. However, OSS projects typically have their own processes and standards, which might not mesh well with a company's internal processes, such as Scrum. This paper presents an experience report from Sony Interactive Entertainment (SIE), where the "toolchain CPU compiler" team directly participates in the "LLVM" OSS project. The team ran into a number of difficulties when using Scrum to manage their development. In particular, the team often failed to complete Scrum sprints where tasks required interaction with the open source community. We look at how the team redefined task flows to alleviate these difficulties, and eventually evolved a highly modified process, dubbed TWINS (This Workflow Is Not Scrum). We assess the revised process, and compare it to other established agile methods, finding it bears a strong resemblance to Scrumban (the SIE team was not aware of Scrumban previously). The TWINS framework presented here may help other organizations who develop software in-house and engage in OSS projects, to gain the best of both worlds.
The Quest for Productivity in Software Engineering: A Practitioners Systematic Literature Review

Software productivity is perceived by practitioners as one of the most important subjects of Software Engineering (SE), because it establishes a connection between technical and economic concerns. Nonetheless, software processes are complex and productivity means different things to different people. In order to realize the full contribution of productivity research to the practice of SE, the compilation and analysis of the diverse practitioner viewpoints and concerns is required. In this paper, we develop a systematic literature review to confirm the existence of different empirical perceptions of productivity from the distinct business sectors and knowledge areas covered in practice by SE, identifying also commonalities that may exist. This review was compiled by analyzing 73 papers on empirical studies published from 1987 to 2017. The review found great variability of study findings, particularly concerning the impacts of agile and hybrid development practices on software productivity, and research gaps that could be investigated in the future.