The factors that affect the performance of open source software development – the perspective of social capital and expertise integration

Drawing on social capital theory, we develop a theoretical model aiming to explore how open source software (OSS) project effectiveness (in terms of team size, team effort and team's level of completion) is affected by expertise integration. This in turn is influenced by three types of social capital – relational capital, cognitive capital and structural capital. In addition, this study also examines two moderating effects – the impact of technical complexity on the relationship between cognitive capital and expertise integration, and of task interdependence on the relationship between expertise integration and task completion. Through a field survey of 160 OSS members from five Taiwanese communities, there is support for some of the proposed hypotheses. Both reciprocity and centrality affect expertise integration as expected, but the influence of commitment and cognitive capital (including expertise and tenure) on expertise integration is not significant. Finally, expertise integration affects both team size and team effort, which in turn jointly influence task completion. This research contributes to advancing theoretical understanding of the effectiveness of free OSS development as well as providing OSS practitioners with insight into how to leverage social capital for improving the performance of OSS development.     

Factors Supporting Knowledge Integration in Global Innovation Projects: An Exploratory Study

This exploratory study identifies the factors which contribute to effective knowledge integration in global innovation projects through an in‐depth analysis of two case studies. Knowledge integration is analysed in two components: knowledge (individual expertise), and integration (working together). Our analysis highlights three distinct ways that individuals think about individual expertise: individual knowledge and competence; feeling valued; and unfamiliar areas. We also identify four ways in which individuals think about working together: good social relations; cultural distance; negotiation skills; and better results through working with others. Using examples from the case studies, we discuss how these factors enable knowledge integration in globally distributed innovation projects. We also propose a model indicating how these factors impact on knowledge integration and innovation performance. This study contributes towards management guidance, which is currently lacking in the area of knowledge integration.     

On multiprocessor task scheduling using efficient state space search approaches

Obtaining an optimal schedule for a set of precedence-constrained tasks is a well-known NP-complete problem in its general form. In view of the intractability of the problem, most of the previous work relies on heuristics that try to find reasonably high quality solutions in an acceptable amount of time. While optimal polynomial-time algorithms are known only for a few simple cases (and in other cases can only be obtained through an exhaustive search with prohibitively high time complexity), they may be critically important for applications in which performance is the prime objective. Optimal solutions can also serve as a reference to test the performance of various heuristics. Moreover, an optimal schedule for a program at hand needs to be determined only once (and off-line) but the program using that schedule is in general executed several times. In this paper, we propose optimal algorithms for static scheduling of task graphs with arbitrary parameters to multiple homogeneous processors. The first algorithm is based on the A^* search technique and uses a computationally efficient cost function for guiding the search with reduced complexity. Additionally, we propose a number of effective state-pruning techniques to reduce the search space. For further lowering the complexity, we propose an efficient parallelization of the search algorithm. We parallelize the algorithm with reduced interprocessor communication as well as with static and dynamic load-balancing schemes to evenly distribute the search states to the processors. We also propose an approximate algorithm that guarantees a bounded deviation from the optimal solution but executes in a considerably shorter time. Based on an extensive experimental evaluation of the algorithms, we conclude that the parallel algorithm with pruning techniques is an efficient scheme for generating optimal solutions of reasonably large problems while the approximate algorithm is effective if slightly degraded solutions are acceptable.     

Mastering map scale: balancing workloads using display and geometry change in multi-scale mapping

This paper builds on a body of European research on multiple resolution data bases (MRDBs), defining a conceptual framework for managing tasks in a multi-scale mapping project. The framework establishes a workload incorporating task difficulty, time to complete a task, required level of expertise, required resources, etc. Project managers must balance the workload among tasks with lower and higher complexity to produce a high quality cartographic product on time and within budget. We argue for increased emphasis on the role of symbol design, which often carries a lower workload than multi-scale mapping based primarily on geometry change. Countering expectations that combining symbol change with geometry change will increase workloads, we argue that in many cases, integration of the two can reduce workloads overall. To demonstrate our points, we describe two case studies drawn from a recent multi-scale mapping and database building project for Ada County, Idaho. We extend the concept of workload balancing, demonstrating that insertion of Level of Detail (LoD) datasets at intermediate scales can further reduce the workload. Previous work proposing LoDs has not reported empirical assessment, and we encourage small and large mapping organizations to contribute to such an effort.     

面向类集成测试序列生成的强化学习研究

集成测试是软件测试过程中不可缺少的步骤, 针对在集成测试中如何对系统中的类合理排序的问题, 国内外研究者提出了多种生成类集成测试序列的方法, 然而他们大多没有将测试桩复杂度作为评估测试代价的指标.针对该问题, 提出面向类集成测试序列生成的强化学习研究方法, 以总体测试桩复杂度为评价测试代价的指标, 生成测试代价尽可能低...     

Modeling Topic-Based Human Expertise for Crowd Entity Resolution

Entity resolution (ER) aims to identify whether two entities in an ER task refer to the same real-world thing. Crowd ER uses humans, in addition to machine algorithms, to obtain the truths of ER tasks. However, inaccurate or erroneous results are likely to be generated when humans give unreliable judgments. Previous studies have found that correctly estimating human accuracy or expertise in crowd ER is crucial to truth inference. However, a large number of them assume that humans have consistent expertise over all the tasks, and ignore the fact that humans may have varied expertise on different topics (e.g., music versus sport). In this paper, we deal with crowd ER in the Semantic Web area. We identify multiple topics of ER tasks and model human expertise on different topics. Furthermore, we leverage similar task clustering to enhance the topic modeling and expertise estimation. We propose a probabilistic graphical model that computes ER task similarity, estimates human expertise, and infers the task truths in a unified framework. Our evaluation results on real-world and synthetic datasets show that, compared with several state-of-the-art approaches, our proposed model achieves higher accuracy on the task truth inference and is more consistent with the human real expertise.     

The effect of individual and task characteristics on decision aid reliance

This research explores reliance behaviours of decision-makers using a decision aid. Objective and subjective task characteristics in the form of task complexity and task difficulty, respectively, are examined, along with the effect of the individual characteristic of expertise. A total of 130 subjects (65 novices and 65 experienced practitioners) completed a lab experiment using a decision aid (Insolve-DG) to help them make decisions for two insolvency tasks with differing levels of complexity. The research finds that the objective task characteristic (task complexity) and individual characteristic (expertise) both affect reliance behaviours; however, their effects are fully mediated by the subjective task characteristic (task difficulty). Expertise and task complexity are both associated with the degree of task difficulty experienced by an individual user: increasing task complexity increases task difficulty, and increasing expertise reduces task difficulty. Task difficulty and task complexity are established as different constructs; and importantly it is task difficulty, not task complexity, that ultimately affects reliance.     

Automated Web navigation using multiagent adaptive dynamic programming

Today a massive amount of information available on the WWW often makes searching for information of interest a long and tedious task. Chasing hyperlinks to find relevant information may be daunting. To overcome such a problem, a learning system, cognizant of a user's interests, can be employed to automatically search for and retrieve relevant information by following appropriate hyperlinks. In this paper, we describe the design of such a learning system for automated Web navigation using adaptive dynamic programming methods. To improve the performance of the learning system, we introduce the notion of multiple model-based learning agents operating in parallel, and describe methods for combining their models. Experimental results on the WWW navigation problem are presented to indicate that combining multiple learning agents, relying on user feedback, is a promising direction to improve learning speed in automated WWW navigation.     

Extensible Prototyping for pragmatic engineering of knowledge-based systems

Knowledge-based systems (KBSs) have been built and practically applied in various contexts for decades. Yet, they still challenge developers by their complexity: Apart from a sound knowledge base they likewise require comprehensive user interface (UI) and interaction design for supporting the task at hand optimally. However, current KBS-Engineering methodologies still mostly focus on knowledge base development, regarding UI/interaction design a more incidental, low priority task. Thus, the UI often is developed in a rather ad hoc manner, thereby neglecting the potential of experimental design and creating reusable solutions. The integration of (rapid) evolutionary prototyping activities with KBS Engineering offers the chance for incorporating UI/interaction design more prominently into the process while at the same time leveraging the overall development task; specifically, we propose Extensible Prototyping as a tailored prototyping approach and we suggest its integration with the Agile Process Model for knowledge-based systems in this paper. For practical support, we further introduce the customized prototyping and knowledge systems engineering tool ProKEt, and we report on practical experiences with applying both the approach and the tool.     

Using visual spatial search interface for WWW applications

Most Internet search engines are keyword-based. They are not efficient for the queries where geographical location is important, such as finding hotels within an area or close to a place of interest. A natural interface for spatial searching is a map, which can be used not only to display locations of search results but also to assist forming search conditions. A map-based search engine requires a well-designed visual interface that is intuitive to use yet flexible and expressive enough to support various types of spatial queries as well as aspatial queries. Similar to hyperlinks for text and images in an HTML page, spatial objects in a map should support hyperlinks. Such an interface needs to be scalable with the size of the geographical regions and the number of websites it covers. In spite of handling typically a very large amount of spatial data, a map-based search interface should meet the expectation of fast response time for interactive applications. In this paper we discuss general requirements and the design for a new map-based web search interface, focusing on integration with the WWW and visual spatial query interface. A number of current and future research issues are discussed, and a prototype for the University of Queensland is presented.     

No interference of task complexity with circadian rhythmicity in a constant routine protocol

Time course in task performance has been studied extensively. In particular, the investigation of circadian rhythmicity in task performance that varied in complexity. However, these studies disclosed heterogeneous outcomes. This could be the result of confounding exogenous factors, the use of diverse tasks, as well as accumulating sleepiness interfering with the underlying circadian drive. The present study varied task demands systematically within a single task and a dual task, using a constant routine protocol to examine the unmasked influence of the endogenous circadian oscillator on the periodicity of performance. Moreover, the subjects were divided into an early-start and a late-start group to estimate the potential interaction of circadian rhythmicity with the duration of prior wakefulness. The results revealed a distinct congruence in the circadian rhythms of all performance measures, with which prior wakefulness (<?40?h) did not interact. Also, single-task as well as dual-task complexity did not interfere with circadian rhythmicity. In conclusion, when sufficiently controlled for masking exogenous factors, task complexity is removed from the underlying circadian rhythms.     

No interference of task complexity with circadian rhythmicity in a constant routine protocol

Time course in task performance has been studied extensively. In particular, the investigation of circadian rhythmicity in task performance that varied in complexity. However, these studies disclosed heterogeneous outcomes. This could be the result of confounding exogenous factors, the use of diverse tasks, as well as accumulating sleepiness interfering with the underlying circadian drive. The present study varied task demands systematically within a single task and a dual task, using a constant routine protocol to examine the unmasked influence of the endogenous circadian oscillator on the periodicity of performance. Moreover, the subjects were divided into an early-start and a late-start group to estimate the potential interaction of circadian rhythmicity with the duration of prior wakefulness. The results revealed a distinct congruence in the circadian rhythms of all performance measures, with which prior wakefulness (< 40 h) did not interact. Also, single-task as well as dual-task complexity did not interfere with circadian rhythmicity. In conclusion, when sufficiently controlled for masking exogenous factors, task complexity is removed from the underlying circadian rhythms.     

An Investigation of Metrics for the In Situ Detection of Software Expertise

Task-based analysis is a common and effective way to measure expertise levels of software users. However, such assessments typically require in-person laboratory studies and inherently require knowledge of the user's task. Today, there is no accepted method for assessing a user's expertise levels outside of a lab, during a user's own home or work environment activities. In this article, we explore the feasibility of software applications automatically inferring a user's expertise levels, based on the user's in situ usage patterns. We outline the potential usage metrics that may be indicative of expertise levels and then perform a study, where we capture such metrics, by installing logging software in the participants' own workplace environments. We then invite those participants into a laboratory study and perform a more traditional task-based assessment of expertise. Our analysis of the study examines if metrics captured in situ, without any task knowledge, can be indicative of user expertise levels. The results show the existence of significant correlations between metrics calculated from in situ usage logs, and task-based user expertise assessments from our laboratory study. We discuss the implications of the results and how future software applications may be able to measure and leverage knowledge of the expertise of its users.     

Relative importance of expertise, lifting height and weight lifted on posture and lumbar external loading during a transfer task in manual material handling

The objective of this study was to measure the effect size of three important factors in manual material handling, namely expertise, lifting height and weight lifted. The effect of expertise was evaluated by contrasting 15 expert and 15 novice handlers, the effect of the weight lifted with a 15-kg box and a 23-kg box and the effect of lifting height with two different box heights: ground level and a 32 cm height. The task consisted of transferring a series of boxes from a conveyor to a hand trolley. Lifting height and weight lifted had more effect size than expertise on external back loading variables (moments) while expertise had low impact. On the other hand, expertise showed a significant effect of posture variables on the lumbar spine and knees. All three factors are important, but for a reduction of external back loading, the focus should be on the lifting height and weight lifted. PRACTITIONER SUMMARY: The objective was to measure the effect size of three important factors in a transfer of boxes from a conveyor to a hand trolley. Lifting height and weight lifted had more effect size than expertise on external back loading variables but expertise was a major determinant in back posture.     

Composition is almost (but not quite) as good as s-1-1

We establish a polynomial upper bound on the time complexity of an s-1-1 function in programming system with a linear-time composition function. This improves the doubly exponential upper bound of Machtey and Young (1978), the only previously known upper bound, and invalidates the belief expressed twice in the literature (Machtey 1978; Machtey and Young 1978) that it could not be significantly improved. We then show our upper bound to be tight, by exhibiting a family of acceptable programming systems for which it is optimal. We deduce several bounds on the time complexity of composition functions, s-1-1 functions, and various other semantic transformations of programs, in programming systems with a linear- or polynomial-time composition function. In particular, we show the existence of an acceptable programming system with a quadratic-time composition function, but no subexponential time s-1-1 function. In one interpretation from Marcoux (1991), this last result states that the complexity of a composition function for an effective programming system does not give an upper bound on the complexity of the “task of programming” in that programming system. By contrast, results by Royer (1987) indicate that this task is essentially no more complex than computing an s-1-1 function.     

Task dynamics in self-organising task groups: expertise, motivational, and performance differences of specialists and generalists

Multi-agent simulation is applied to explore how different types of task variety cause workgroups to change their task allocation accordingly. We studied two groups, generalists and specialists. We hypothesised that the performance of the specialists would decrease when task variety increases. The generalists, on the other hand, would perform better in a high task variety condition. The results show that these hypotheses were only partly supported because both learning and motivational effects changed the task allocation process in a much more complex way. We conclude that although no task variety leads to specialisation and high task variety leads to generalisation, in general, performance is better when task variety is low. Further, in case of no task variety, specialists outperform generalists. In case of moderate variety the opposite is true. With high task variety, since there is no space for any expertise and motivational development, the behaviour of specialists and generalists becomes more similar, and, consequently also their performance.     

Visual language integration: A survey and open challenges

With the recent development of deep learning technology comes the wide use of artificial intelligence (AI) models in various domains. AI shows good performance for definite-purpose tasks, such as image recognition and text classification. The recognition performance for every single task has become more accurate than feature engineering, enabling more work that could not be done before. In addition, with the development of generation technology (e.g., GPT-3), AI models are showing stable performances in each recognition and generation task. However, not many studies have focused on how to integrate these models efficiently to achieve comprehensive human interaction. Each model grows in size with improved performance, thereby consequently requiring more computing power and more complicated designs to train than before. This requirement increases the complexity of each model and requires more paired data, making model integration difficult. This study provides a survey on visual language integration with a hierarchical approach for reviewing the recent trends that have already been performed on AI models among research communities as the interaction component. We also compare herein the strengths of existing AI models and integration approaches and the limitations they face. Furthermore, we discuss the current related issues and which research is needed for visual language integration. More specifically, we identify four aspects of visual language integration models: multimodal learning, multi-task learning, end-to-end learning, and embodiment for embodied visual language interaction. Finally, we discuss some current open issues and challenges and conclude our survey by giving possible future directions.     

A text mining approach for automatic construction of hypertexts

The research on automatic hypertext construction emerges rapidly in the last decade because there exists a urgent need to translate the gigantic amount of legacy documents into web pages. Unlike traditional ‘flat’ texts, a hypertext contains a number of navigational hyperlinks that point to some related hypertexts or locations of the same hypertext. Traditionally, these hyperlinks were constructed by the creators of the web pages with or without the help of some authoring tools. However, the gigantic amount of documents produced each day prevent from such manual construction. Thus an automatic hypertext construction method is necessary for content providers to efficiently produce adequate information that can be used by web surfers. Although most of the web pages contain a number of non-textual data such as images, sounds, and video clips, text data still contribute the major part of information about the pages. Therefore, it is not surprising that most of automatic hypertext construction methods inherit from traditional information retrieval research. In this work, we will propose a new automatic hypertext construction method based on a text mining approach. Our method applies the self-organizing map algorithm to cluster some at text documents in a training corpus and generate two maps. We then use these maps to identify the sources and destinations of some important hyperlinks within these training documents. The constructed hyperlinks are then inserted into the training documents to translate them into hypertext form. Such translated documents will form the new corpus. Incoming documents can also be translated into hypertext form and added to the corpus through the same approach. Our method had been tested on a set of at text documents collected from a newswire site. Although we only use Chinese text documents, our approach can be applied to any documents that can be transformed to a set of index terms.     

Effects of Personality on Pair Programming

Personality tests in various guises are commonly used in recruitment and career counseling industries. Such tests have also been considered as instruments for predicting the job performance of software professionals both individually and in teams. However, research suggests that other human-related factors such as motivation, general mental ability, expertise, and task complexity also affect the performance in general. This paper reports on a study of the impact of the Big Five personality traits on the performance of pair programmers together with the impact of expertise and task complexity. The study involved 196 software professionals in three countries forming 98 pairs. The analysis consisted of a confirmatory part and an exploratory part. The results show that: 1) Our data do not confirm a meta-analysis-based model of the impact of certain personality traits on performance and 2) personality traits, in general, have modest predictive value on pair programming performance compared with expertise, task complexity, and country. We conclude that more effort should be spent on investigating other performance-related predictors such as expertise, and task complexity, as well as other promising predictors, such as programming skill and learning. We also conclude that effort should be spent on elaborating on the effects of personality on various measures of collaboration, which, in turn, may be used to predict and influence performance. Insights into such malleable, rather than static, factors may then be used to improve pair programming performance.