Using network analysis metrics to discover functionally important methods in large-scale software systems

Recent studies focused on the importance of adopting network analysis approaches such as social network analysis in the supply chain networks to better understand and manage the roles of organizations in inter-organizational relationships. The main aim of this research is to identify and integrate network analysis metrics in the existent literature in this realm which is applicable to characterize the position and role of organizations in the supply chain network context and their impact on the behavior and outcomes of organizations and the whole supply chain network. To this aim, we followed a systematic literature review process using Scopus database to identify high-quality papers through several screening stages. Our findings illustrate that there are two main sources of interfirm differences including atomistic properties and relational properties. With an emphasis on relational properties through the lens of network analysis metrics, we integrated influential characteristics on actor’s behavior and performance into three main categories of node level, tie level, and network level. Our findings are applicable to address any emergent phenomenon and the roles of actors based on their position in the network context such as supply chain network and study their behavior and performance.

As the evolution of applications, software systems become more and more complex. However, it is a challenge to deliver easy-to-use and reliable large-scale distributed software systems. This paper first reviews current requirements on software systems and the progress made and then reports our effort in delivering a more usable and reliable large-scale distributed software development framework (iFrame) to support e-research. Bearing human collaboration in the first place, iFrame synthesizes peer-to-peer (P2P), service-oriented architecture (SOA), Web 2.0 and semantic Web into grid: P2P is deployed to construct an interconnected environment to enhance reliability and scalability, SOA and semantic Web are exploited to deliver concrete business functionality and prompt interoperability, whereas Web 2.0 is utilized to devise more usable interface.

A network-based perspective on designing permits research on the complexity of product, process, and people interactions. Strengthened by the latest advances in information technologies and accessibility of data, a network-based perspective and use of appropriate network analysis metrics, theories, and tools allow us to explore new data-driven research approaches in design. These approaches allow us to move from counting to connecting, meaning to explicitly link disconnected pieces of data, information, and knowledge, and thus to answer far-reaching research questions with strong industrial and societal impact. This chapter contributes to the use of network science in empirical studies of design organisations. It focuses on introducing a network-based perspective on the design process and in particular on making use of network science to support design research and practice. The main contribution of this chapter is an overview of the methodological challenges and core decision points when embarking on network-based design research, namely defining the overall research purpose and selecting network features. We furthermore highlight the potential for using archival data, the opportunities for navigating different levels of the design process that network analysis permits, what we here call zooming in and out, and the use of network visualisations. We illustrate the main points with a case from our own research on engineering communication networks. In this case, we have used more than three years of archival data, including design activity logs and work-related email exchanges from a recently completed large-scale engineering systems project of designing and developing a renewable power plant.

Software systems play a key role in most businesses nowadays. Building robust, reliable and scalable software systems require going through a software production cycle (or process). However, it has been noticed that software systems are subjected to changes, whether those amendments are important or not. Those changes to software systems are viewed as a considerable issue in software engineering; they are considered as a burden and cost a lot, especially in cases such as enterprises and large-scale software systems. This study aims to identify the reasons that cause software changes and suggest remedies for those reasons. We survey the opinions of experts such as technical managers, team leaders, and senior developers. We collected 81 responses to our questionnaire, which aimed to establish common software development practices in the local industry. We also conducted 16 semi-structured interviews targeting the most senior experts, in which we directly discussed the reasons and remedies for software changes. Our results highlight the most influential reasons that cause changes to software systems, such as changes to user requirements, requests for new features, software development methodology, solving bugs, refactoring, and weak user experience design. Most importantly, the study solicited solutions that can reduce the need for software changes

To assure high software quality for large-scale industrial software systems, traditional approaches of software quality assurance, such as software testing and performance engineering, have been widely used within Alibaba, the world's largest retailer, and one of the largest Internet companies in the world. However, there still exists a high demand for software quality assessment to achieve high sustainability of business growth and engineering culture in Alibaba. To address this issue, we develop an industrial solution for software quality assessment by following the GQM paradigm in an industrial setting. Moreover, we integrate multiple assessment methods into our solution, ranging from metric selection to rating aggregation. Our solution has been implemented, deployed, and adopted at Alibaba: (1) used by Alibaba's Business Platform Unit to continually monitor the quality for 60+ core software systems; (2) used by Alibaba's R&D Efficiency Unit to support group-wide quality-aware code search and automatic code inspection. This paper presents our proposed industrial solution, including its techniques and industrial adoption, along with the lessons learned during the development and deployment of our solution.

The purpose of this study was to identify fault-prone functions that are likely to contain faults in a given software system. Five metrics were used: Di, an internal design metric which incorporates factors related to a function's internal structure; De, an external design metric which focuses on a function's external relationships to the rest of the software system; D(G), a composite design metric which is a linear combination of Di and De; and the union and intersection of Di, De, and D(G). Since the system being considered was already developed, a very important aspect of our study was to extract the design information directly from the source code rather than from the corresponding design documentation which may not exist or, if it does exist, it may be incomplete, difficult to understand, or not updated. To make the analysis more accurate and efficient, a metric analysis tool (χMetrics) was implemented. We conducted experiments using χMetrics on part of a distributed software system, written in C, with a client–server architecture, and identified a small percentage of its functions as good candidates for fault-proneness. Files containing these functions were then validated by the real defect data collected between a recent major release and its subsequent release for their fault-proneness. The results indicate that our metrics are good indicators of fault-prone functions. Two extra experiments were also conducted to show that function size cannot replace any of our metrics; and where the function size was factored out our metrics performed better than the normalized metrics. The important benefit of our metrics is that they help project managers determine where additional testing effort should be spent and possibly which fault-prone functions should be assigned to more experienced programmers if modifications are required. Copyright © 2000 John Wiley & Sons, Ltd.

In the past few years, research has been actively carried out in an attempt to improve the quality and reliability of large-scale software systems. Although progress has been made on the formal proof of program correctness, proving large-scale software systems correct by formal proof is still many years away. Automated software tools have been found to be valuable in improving software reliability and attacking the high cost of software systems. This paper attempts to describe some main features of automated software tools and some software evaluation systems that are currently available.

Important author analysis is one of the key issues in the research professionals’ relationship network. Research professionals’ relationship network is a type of social network which is constitute of research professionals and there co-author relationship with other professionals. So many social network analysis metrics are available to analyze the important or prominent actor in the network. Centrality in social network analysis represents prestige or importance of a node with respect to other nodes in the network and also represents the importance of relationship between nodes. In this paper, we studied social network theory to understand how the collaboration of research professionals has impact in research world and performance of individual researcher. For this analysis, we use social network analysis metrics like normalize degree centrality, closeness centrality, betweenness centrality and eigenvector centrality.KeywordsSocial networkResearcher relationCentrality

Production of a large-scale software system involves quite a few software components. It is very common to develop such software components in a distributed environment consisting of multiple computer systems connected by computer networks. In order to support effective software development in a distributed environment, a mechanism to combine a large number of components, which are distributed over the network, into a single system in a systematic way is quite essential. Object Make is a tool that automates the building process of software systems consisting of software components that are stored in the file systems of distributed computer systems, as if they are stored in a single file system. Software developers are not required to transfer these remote components to their own local computer systems explicitly, even if the computer systems do not share a common file system. The paper gives an overview of the distributed software build process for large-scale software systems using Object Make.

Despite recent progress in automated test generation research, significant challenges remain for applying these techniques on large-scale software systems. These systems under test often require structurally complex test inputs within a large input domain. It is challenging to automatically generate a reasonable number of tests that are both legal and behaviorally-diverse to exercise these systems. Constraint-based test generation is an effective approach for generating structurally complex inputs for systematic testing. While this approach can typically generate large numbers of tests, it has limited scalability – tests generated are usually only up to a small bound on input size. Combinatorial test generation, e.g., pair-wise testing, is a more scalable approach but is challenging to apply on commercial software systems that require complex input structures that cannot be formed by using arbitrary combinations. This paper introduces comKorat, which unifies constraint-based generation of structurally complex tests with combinatorial testing. Specifically, comKorat integrates Korat and ACTS test generators to generate test suites for large scale software systems with structurally complex test inputs. We have successfully applied comKorat on four software applications developed at eBay and Yahoo!. The experimental results show that comKorat outperforms existing solutions in execution time and test coverage. Furthermore, comKorat found a total of 59 previously unknown bugs in the four applications.

Software systems are important components for the deployment and operation of large-scale critical systems, such as nuclear plants and aerospace shuttles. The very high reliability is critically required. Due to the very large scale and computational complexity, however, there are some inherent challenges for conducting extensive testing. In this paper, we define such challenges in the testing scope of large-scale critical software. The paper presents the testing workflows and coverage, the mechanism of random testing, followed by the automated testing framework. We then present the implementing details of the automated testing platform ATLAS.

Data engineering and software engineering have a very close relationship. Our ability in developing and maintaining large-scale software mainly depends on our ability to understand, analyze, use and update the enormous amount of data associated with various phases of the life cycle of the large-scale software system under consideration. Although many tools to provide useful assistance in developing and maintaining software are available, they are often not effective for large-scale software due to the complexity in dealing with the very large amount of data. One way to overcome this difficulty is to develop a knowledge-based software environment so that artificial intelligence techniques can be applied to large-scale software development and maintenance. On the other hand, the effectiveness and quality of data engineering depend upon the performance of the software engineering approaches and tools used in data engineering. In this panel session, the panelists will discuss various aspects of this subject and exchange their ideas and experiences in this area. Ample time will be available for discussion from the floor.

Discovering power laws in computer programs

The new trend is to move from the traditional software development approach, which focuses on building software systems from scratch, to component based software development approach, which revolutionizes how software systems are built. The focus of this new approach is on development of new systems, by selecting and assembling a set of off the shelf components within an appropriate software architecture. On one hand, the use of off the shelf components has led to a great potential for: (1) significantly reducing cost and time to market of large scale and complex software systems; (2) improving system maintainability and flexibility by allowing new components to replace old ones; and (3) enhancing system quality by allowing components to be developed by those who are specialized in the application area and systems to be built by software engineers who are specialized in component based software development. On the other hand, the use of commercial off the shelf software-delivered as black box components-has raised a few major technical and non technical issues. The paper explores those issues, and discusses several directions for future research that would help to expand the use of component based software development approach.

Construction is considered to be one of the most dangerous industries due to its wide variety of accidents and high risk. Although people have been using various means to improve safety levels, the frequent occurrence of accidents still plagues the development of this industry. Accurately grasping the characteristics and analyzing the complex relationships between the risks of different accident types are the keys to a comprehensive understanding and promotion of construction safety. Simultaneously, we need to note that the level and frequency of accidents affects the risk characteristics, but previous studies have not paid sufficient attention to these features. Using complex network theory, based on the risk factors and risk triggering relationship extracted from construction accidents and considering the accident level and the frequency of occurrences as their weights, several weighted risk networks are generated according to different accident types. Then, several network analysis metrics are introduced to reveal the characteristics of the risk network and identify the key risk factors. The analytical framework helps to pose targeted suggestions to reduce the occurrence of frequent major accidents.