Jianwen Xiang

A Survey on Knowledge Management in Software Engineering

Software development is a knowledge intensive and collaborative activity. The success of the project totally depends on knowledge and experience of the developers. Increasing knowledge creation and sharing among software engineers are uphill tasks in software development environments. The field of knowledge management has emerged into this field to improve the productivity of the software by effective and efficient knowledge creation, sharing and transferring. In other words, knowledge management for software engineering aims at facilitating knowledge flow and utilization across every phases of a software engineering process. Therefore, adaptation of various knowledge management practices by software engineering organizations is essential. This survey identified the knowledge management involvement in software engineering in different perspectives in the recent literature and guide future research in this area.

Exploring Academic Knowledge Creation Models for Graduate Researches

Knowledge management theories and tools have been developed rapidly in the field of business administration. However, since their application in the academic field is still very limited, this paper focuses on knowledge creation in the academic field. It is usually difficult for young graduate students to imagine their research process, which proceeds from a small idea to a research paper. Wierzbicki and Nakamori [1] introduced three knowledge creation models for three different academic fields, which are normative or hypothetical models to be verified in applications. This paper modified these models by interviewing graduate students and their supervisors, who are doing research in four fields in knowledge science. This study made clear the knowledge gaps between graduate students and their supervisors.

Knowledge synthesis in software industries: a survey in Sri Lanka

Abstract Software development is a knowledge intensive and a collaborative activity, which mainly depends on knowledge and experience of the software developers. Knowledge management in software industries aims to create an environment for continuous knowledge sharing and creation to remain competitive. This study investigates the current status of knowledge management in three software industries in Sri Lanka on the basis that they claimed to apply knowledge management practices within their organizations using both quantitative and qualitative methods. The knowledge synthesis theory is used in this study to synthesize a variety of knowledge, unseen problems, obstructions, and the corresponding underlying reasons in implementing the knowledge management practices. Findings of this study emphasized that while knowledge management practices were considered to be important, organizational learning and culture, attitudes and behaviors of employees, information technology infrastructure, and rewards systems were inadequate to address an effective management of knowledge in these three software industries. One of the findings showed that, software developers believe both software engineering knowledge and information communication technology are considered to be an enterprise’s strategic asset and a main source to create competitive advantage. Findings also presented a compelling evidence of the soundness for the need of knowledge management practices to manage software engineering knowledge.

A Refined Algorithm for Reachability Analysis of Updatable Timed Automata

Timed automata (TAs) are a widely-used model for formally analyzing real-time systems. Updatable Timed Automata (UTAs), extended from TAs, provide a more flexible way to adjust the value of clock during location switches. The reachability problem of some subclasses of UTAs is decidable. When considering an efficient implementation of these subclasses, the algorithm that is usually used is based on the notion of zones, implemented by Difference Bound Matrices (DBMs). However, similar to that of TAs, the zone-based algorithm is not sound when considering diagonal constraints on clocks, illustrated by a well-known counterexample. This paper proposes a sound zone-based algorithm by introducing extended DBM (EDBM). It refines the standard forward reachability analysis (FRA) algorithm. The correctness of the algorithm is proven and experimental results are given to show the efficiency of the implementation.

Analysis of Software Aging in Android

There are a number of researches on software aging in different systems, but few researches have been done in Android system. At the same time, due to the complexity of Android memory management mechanisms and lack of comprehensive documents, it is difficult to analyze corresponding memory management mechanisms to determine whether there is aging. Based on these, we focus on some aging-related phenomena by observing available memory of Android, and use experiments to progressively verify some hypotheses about aging in Android. Then, we carry out mathematical modeling of aging and predictive analysis with Markov chains. Through our experiments and modeling analysis, we observe the existence of Android aging, and find that aging is generally irreversible and that the warm rejuvenation (application restart) has little effect on the recovery or mitigation of aging problem. The current experiments are preliminary and limited to specific version of Android OS, smartphone, and applications, but the results and observations may provide a new perspective for further study on aging and rejuvenation in Android with more statistically robust experiments.

Handling Unreasonable Data in Negative Surveys

Negative survey is a method of collecting sensitive data. Compared with traditional surveys, negative survey can effectively protect the privacy of participants. Data collector usually has some background knowledge about the survey, and background knowledge could be effectively used for estimating aggregated results from the collected data. Traditional methods for estimating aggregated results would get some unreasonable data, such as negative values, and some values inconsistent with the background knowledge. Handling these unreasonable data could improve the accuracy of the estimated aggregated results. In this paper, we propose a method for handling values that are inconsistent with the background knowledge and negative values. The simulation results show that, compared with NStoPS, NStoPS-I and NStoPS-BK, more accurate aggregated results could be estimated by the proposed method.

Iris Template Protection Based on Local Ranking

Biometrics have been widely studied in recent years, and they are increasingly employed in real-world applications. Meanwhile, a number of potential threats to the privacy of biometric data arise. Iris template protection demands that the privacy of iris data should be protected when performing iris recognition. According to the international standard ISO/IEC 24745, iris template protection should satisfy the irreversibility, revocability, and unlinkability. However, existing works about iris template protection demonstrate that it is difficult to satisfy the three privacy requirements simultaneously while supporting effective iris recognition. In this paper, we propose an iris template protection method based on local ranking. Specifically, the iris data are first XORed (Exclusive OR operation) with an application-specific string; next, we divide the results into blocks and then partition the blocks into groups. The blocks in each group are ranked according to their decimal values, and original blocks are transformed to their rank values for storage. We also extend the basic method to support the shifting strategy and masking strategy, which are two important strategies for iris recognition. We demonstrate that the proposed method satisfies the irreversibility, revocability, and unlinkability. Experimental results on typical iris datasets (i.e., CASIA-IrisV3-Interval, CASIA-IrisV4-Lamp, UBIRIS-V1-S1, and MMU-V1) show that the proposed method could maintain the recognition performance while protecting the privacy of iris data.

A Rejuvenation Strategy in Android

Compared to critical systems, the user of Android is more concerned about the application launch time. It needs to perform rejuvenation to improve the user experience when the Android suffers from software aging that would lead to an increase of the launch time. Also, rejuvenation can incur in some costs. So it is important to make an optimal rejuvenation strategy to minimize these costs and improve the user experience. At the same time, different workloads on Android have different aging phenomena, which may influence the validation of system state. Based on these, we carried out experiments to simulate user behavior on Android and find software aging phenomena in Android. Then we used active learning based on random forest and four states to build model and constructed software aging rejuvenation strategy on Android. Comparing the experiments without rejuvenation, there is an obvious decrease of the average of application launch time after using the rejuvenation strategy, and the running time of experiments with rejuvenation is longer than that without rejuvenation, which improves the user experience and reduces the probability of unexpected errors.

An Ontology-Based Knowledge Framework for Software Testing

Software development is conceptually a complex, knowledge intensive and a collaborative activity, which mainly depends on knowledge and experience of the software developers. Effective software development relies on the knowledge collaboration where each and every software engineer shares his or her knowledge or acquires knowledge from others. Software testing which is a sub area of software engineering is related to various activities such as test planning, test case design, test implementation, test execution and test result analysis and they are all essential. Given great importance to knowledge for software testing, and the potential benefits of managing software testing knowledge, an ontological approach to represent the necessary software testing knowledge within the software testers context was developed. Using this approach, software testing ontology to include information needs identified for the software testing activities was designed. Competency questions (contextualized information) were used to determine the scope of the ontology and used to identify the contents of the ontology because contextualized information fulfills the expressiveness and reasoning requirements of the software testing ontology. SPARQL query was used to query the competency questions. A web based KM Portal was developed using semantic web technologies for knowledge representation. Software testers can annotate their testing knowledge with the support of ISTQB and IEEE 829-2008 terms. Both ontology experts and non-experts evaluated the developed ontology. We believe our software testing ontology can support other software organizations to improve the sharing of knowledge and learning practices.

A Comparative Study of Knowledge Management on Undergraduate by Questionnaire

Compared with other areas, knowledge management in education area has been lack of attention for a long time. This paper did a survey on undergraduate in a Chinese research university. By designing the questionnaire, implementing the survey website by programming, we got 515 original data from art students and science students. Then we analyzed the data by several data-mining algorithms, e.g. the optimal scale regression analysis, the bivariate correlation analysis and the K-center cluster analysis, and we got some significant conclusions and provided some scientific solutions. Compared with traditional educational management, this paper surveyed art and science students comprehensively and made a comparative study between them. In addition, we used data-mining algorithms to process the data. The results of this paper could be used to improve the knowledge management and the quality of higher education to some extent.