ContextSoftware metrics may be used in fault prediction models to improve software quality by predicting fault location. ObjectiveThis paper aims to identify software metrics and to assess their applicability in software fault prediction. We investigated the influence of context on metrics' selection and performance. MethodThis systematic literature review includes 106 papers published between 1991 and 2011. The selected papers are classified according to metrics and context properties. ResultsObject-oriented metrics (49%) were used nearly twice as often compared to traditional source code metrics (27%) or process metrics (24%). Chidamber and Kemerer's (CK) object-oriented metrics were most frequently used. According to the selected studies there are significant differences between the metrics used in fault prediction performance. Object-oriented and process metrics have been reported to be more successful in finding faults compared to traditional size and complexity metrics. Process metrics seem to be better at predicting post-release faults compared to any static code metrics. ConclusionMore studies should be performed on large industrial software systems to find metrics more relevant for the industry and to answer the question as to which metrics should be used in a given context.

http://romisatriawahono.net/lecture/rm/survey/software%20engineering/Software%20Fault%20Defect%20Prediction/Radjenovic%20-%20Software%20fault%20prediction%20metrics%20-%202013.pdf

Software fault prediction metrics

Context: Software development effort estimation (SDEE) is the process of predicting the effort required to develop a software system. In order to improve estimation accuracy, many researchers have proposed machine learning (ML) based SDEE models (ML models) since 1990s. However, there has been no attempt to analyze the empirical evidence on ML models in a systematic way. Objective: This research aims to systematically analyze ML models from four aspects: type of ML technique, estimation accuracy, model comparison, and estimation context. Method: We performed a systematic literature review of empirical studies on ML model published in the last two decades (1991-2010). Results: We have identified 84 primary studies relevant to the objective of this research. After investigating these studies, we found that eight types of ML techniques have been employed in SDEE models. Overall speaking, the estimation accuracy of these ML models is close to the acceptable level and is better than that of non-ML models. Furthermore, different ML models have different strengths and weaknesses and thus favor different estimation contexts. Conclusion: ML models are promising in the field of SDEE. However, the application of ML models in industry is still limited, so that more effort and incentives are needed to facilitate the application of ML models. To this end, based on the findings of this review, we provide recommendations for researchers as well as guidelines for practitioners.

http://romisatriawahono.net/lecture/rm/survey/software%20engineering/Software%20Cost%20Effort%20Estimation/Wen%20-%20software%20development%20effort%20estimation%20models%20-%202012.pdf

Systematic literature review of machine learning based software development effort estimation models

Context: Software defect prediction studies usually built models using within-company data, but very few focused on the prediction models trained with cross-company data. It is difficult to employ these models which are built on the within-company data in practice, because of the lack of these local data repositories. Recently, transfer learning has attracted more and more attention for building classifier in target domain using the data from related source domain. It is very useful in cases when distributions of training and test instances differ, but is it appropriate for cross-company software defect prediction? Objective: In this paper, we consider the cross-company defect prediction scenario where source and target data are drawn from different companies. In order to harness cross company data, we try to exploit the transfer learning method to build faster and highly effective prediction model. Method: Unlike the prior works selecting training data which are similar from the test data, we proposed a novel algorithm called Transfer Naive Bayes (TNB), by using the information of all the proper features in training data. Our solution estimates the distribution of the test data, and transfers cross-company data information into the weights of the training data. On these weighted data, the defect prediction model is built. Results: This article presents a theoretical analysis for the comparative methods, and shows the experiment results on the data sets from different organizations. It indicates that TNB is more accurate in terms of AUC (The area under the receiver operating characteristic curve), within less runtime than the state of the art methods. Conclusion: It is concluded that when there are too few local training data to train good classifiers, the useful knowledge from different-distribution training data on feature level may help. We are optimistic that our transfer learning method can guide optimal resource allocation strategies, which may reduce software testing cost and increase effectiveness of software testing process.

Transfer learning for cross-company software defect prediction

Context: Software startups are newly created companies with no operating history and fast in producing cutting-edge technologies. These companies develop software under highly uncertain conditions, tackling fast-growing markets under severe lack of resources. Therefore, software startups present a unique combination of characteristics which pose several challenges to software development activities. Objective: This study aims to structure and analyze the literature on software development in startup companies, determining thereby the potential for technology transfer and identifying software development work practices reported by practitioners and researchers. Method: We conducted a systematic mapping study, developing a classification schema, ranking the selected primary studies according their rigor and relevance, and analyzing reported software development work practices in startups. Results: A total of 43 primary studies were identified and mapped, synthesizing the available evidence on software development in startups. Only 16 studies are entirely dedicated to software development in startups, of which 10 result in a weak contribution (advice and implications (6); lesson learned (3); tool (1)). Nineteen studies focus on managerial and organizational factors. Moreover, only 9 studies exhibit high scientific rigor and relevance. From the reviewed primary studies, 213 software engineering work practices were extracted, categorized and analyzed. Conclusion: This mapping study provides the first systematic exploration of the state-of-art on software startup research. The existing body of knowledge is limited to a few high quality studies. Furthermore, the results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

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Software development in startup companies: A systematic mapping study

Context: During requirements engineering, prioritization is performed to grade or rank requirements in their order of importance and subsequent implementation releases It is a major step taken in making crucial decisions so as to increase the economic value of a system Objective: The purpose of this study is to identify and analyze existing prioritization techniques in the context of the formulated research questions Method: Search terms with relevant keywords were used to identify primary studies that relate requirements prioritization classified under journal articles, conference papers, workshops, symposiums, book chapters and IEEE bulletins Results: 73 Primary studies were selected from the search processes Out of these studies; 13 were journal articles, 35 were conference papers and 8 were workshop papers Furthermore, contributions from symposiums as well as IEEE bulletins were 2 each while the total number of book chapters amounted to 13 Conclusion: Prioritization has been significantly discussed in the requirements engineering domain However, it was generally discovered that, existing prioritization techniques suffer from a number of limitations which includes: lack of scalability, methods of dealing with rank updates during requirements evolution, coordination among stakeholders and requirements dependency issues Also, the applicability of existing techniques in complex and real setting has not been reported yet

A systematic literature review of software requirements prioritization research

Context: During requirements engineering, prioritization is performed to grade or rank requirements in their order of importance and subsequent implementation releases. It is a major step taken in making crucial decisions so as to increase the economic value of a system. Objective: The purpose of this study is to identify and analyze existing prioritization techniques in the context of the formulated research questions. Method: Search terms with relevant keywords were used to identify primary studies that relate requirements prioritization classified under journal articles, conference papers, workshops, symposiums, book chapters and IEEE bulletins. Results: 73 Primary studies were selected from the search processes. Out of these studies; 13 were journal articles, 35 were conference papers and 8 were workshop papers. Furthermore, contributions from symposiums as well as IEEE bulletins were 2 each while the total number of book chapters amounted

https://romisatriawahono.net/lecture/rm/survey/software%20engineering/Requirement%20Engineering/Achimugu%20-%20Software%20Requirements%20Prioritization%20-%202014.pdf

Information and Software Technology

Higher education is approaching the point at which Science and Technology particularly Information and Communication Technology (ICT), plays a vital role in nearly all phases of the educational process. The Internet has emerged as a major driving force of this dynamic development of Information and Communication Technologies (ICTs) which has impacted positively in virtually every sector of the Nigerian economy. In the education sector, tertiary institutions use computers in their academic programs in order to produce good quality of research output and learning. This paper focuses on how ICT diffusion has impacted the higher educational sector positively in Nigeria. The research also exposes the effect of ICT diffusion on undergraduate and postgraduate students of Nigeria’s tertiary institutions. A combination of observation, interview and document materials for data gathering was employed as methodology for carrying out this research. The result of the research suggests that ICT is becoming a driving force for educational reforms and that ICTs have become an integrative part of national education policies and plans in Nigerian tertiary institutions.

An Evaluation of the Impact of ICT Diffusion in Nigeria's Higher Educational Institutions

Information Technology (IT) refers to several forms of information exchange between two or more computers through any of the several methods of interconnection, principally the Internet. These technologies provide speedy, inexpensive and convenient means of communication. The diffusion of these technologies in many countries by different sectors of the economy have been found to have direct positive impact on the organization’s efficiency and have led to more rapid acceleration of development in these countries. In developing countries Nigeria precisely, preliminary investigations show that only a few organizations in the economy have adopted the IT, but there has not been formal study to determine the level of diffusion and the factors affecting IT diffusion as well as impact on the efficiency of the organizations. This study, is therefore, designed to determine the level of diffusion in the Nigerian economy, and the impact on the operations of these organizations as well as investigating the factors responsible for the present level of diffusion of these technologies in the economy.

Adoption of Information & Communication Technologies in Developing Countries: An Impact Analysis

Progress of health information system (HIS) in recent years has made significant impact on both developed and developing countries. HIS has played a very important role in the hospital. Construction and employment of HIS can improve the efficiency and quality of healthcare work. But the development of HIS has some problems inherent in them such as non-standard hospital management, poor standardization, and lack of interoperable software development. As a result, HIS run is not able to share medical information and therefore, can’t meet the needs of reform in healthcare system delivery. In the future, for the sake of medical information sharing, teleconsulation, hospital efficiency enhancement, all the independent systems will realize interoperability. This paper however, seeks to analyze and address factors militating against interoperability in software systems the healthcare domain and recommends the adoption of service oriented architecture (SOA) within the domain.

https://www.jhidc.org/index.php/jhidc/article/download/60/95

Philip Achimugu

Papers

A systematic literature review of software requirements prioritization research

Information and Software Technology

An Evaluation of the Impact of ICT Diffusion in Nigeria's Higher Educational Institutions

Adoption of Information & Communication Technologies in Developing Countries: An Impact Analysis

Lack of Interoperable Health Information Systems in Developing Countries: An Impact Analysis