• We propose a graph neural network-based model for long method and blob code smell detection. • The best strategies for the class imbalance of graph data and graph pooling are determined through experiments in our method. • During model design for abstract syntax tree of code, Euclidean space and non-Euclidean space are combined. • The experiments show that our proposed method outperforms machine learning methods and deep learning methods. The concept of code smell was first proposed in the late nineties, to refer to signals that code may need refactoring. While not necessarily affecting functionality, code smell can hinder understandability and future scalability of the program. As a result, the precise detection of code smell has become an important topic in coding research. However, current detection methods are limited by imbalanced and industrial-irrelevant datasets, a lack of sufficient structural and logical information on the code, and simple model architecture. Given these limitations, this paper utilized an industry-relevant and sufficient dataset and then developed a graph neural network to better detect code smell. First, we identified Long Method and Blob as our research subjects due to their frequent occurrence and impacts on the maintainability of software. We then designed modified fuzzy sampling with focalloss to address the issue of data imbalance. Second, to deal with the large volume of data, we proposed a global and local attention scoring mechanism to extract the key information from the code. Third, in order to design a graph neural network specifically for the abstract syntax tree of code, we combined Euclidean space and non-Euclidean space. Finally, we compared our method with other machine learning methods and deep learning methods. The results demonstrate that our method outperforms the other methods on Long Method and Blob, which indicates the effectiveness of our proposed method.

This innovative practice WIP paper describes a research study that explores the integration of ChatGPT into the software testing curriculum and evaluates its effectiveness compared to human-generated testing artifacts. In a Capstone Project course, students were tasked with generating preparatory testing artifacts using ChatGPT prompts, which they had previously created manually. Their understanding and the effectiveness of the Artificial Intelligence generated artifacts were assessed through targeted questions. The results, drawn from this in-class assignment at a North American community college indicate that while ChatGPT can automate many testing preparation tasks, it cannot fully replace human expertise. However, students already familiar with Information Technology at the postgraduate level, found the integration of ChatGPT into their workflow to be straightforward. The study suggests that AI can be gradually introduced into software testing education to keep pace with technological advancements.

Testing is an indispensable part of software development. However, a career in software testing is reported to be unpopular among technology students. This can potentially create a shortage of testers in the software industry in the future. The question is, whether the perception that undergraduate students have about software testing is accurate and whether it differs from the experience reported by those who work in testing activities in the software development industry. We obtained 63 answers from practitioners in the software industry, and previous surveys analyzed the perceptions of 648 students from software engineering. This investigation demonstrates that a career in software testing is more exciting and rewarding, as reported by professionals working in the field, than students may believe. Therefore, in order to guarantee a workforce focused on software quality, the academy and the software industry need to work together to better inform students about software testing and its essential role in software development. In particular, courses in testing automation are expected to increase the popularity of the area among students.

Nowadays, software testing professionals are commonly required to develop coding skills to work on test automation. One essential skill required from those who code is the ability to implement code refactoring, a valued quality aspect of software development; however, software developers usually encounter obstacles in successfully applying this practice. In this scenario, the present study aims to explore how software testing professionals (e.g., software testers, test engineers, test analysts, and software QAs) deal with code refactoring to understand the benefits and limitations of this practice in the context of software testing. We followed the guidelines to conduct surveys in software engineering and applied three sampling techniques, namely convenience sampling, purposive sampling, and snowballing sampling, to collect data from testing professionals. We received answers from 80 individuals reporting their experience refactoring the code of automated tests. We concluded that in the context of software testing, refactoring offers several benefits, such as supporting the maintenance of automated tests and improving the performance of the testing team. However, practitioners might encounter barriers in effectively implementing this practice, in particular, the lack of interest from managers and leaders. Our study raises discussions on the importance of having testing professionals implement refactoring in the code of automated tests, allowing them to improve their coding abilities.

This paper explores the role of continual learning strategies when neural networks are confronted with learning tasks sequentially. We analyze the stability-plasticity dilemma with three factors in mind: the type of network architecture used, the continual learning scenario defined and the continual learning strategy implemented. Our results show that complementary learning systems and neural volume significantly contribute towards memory retrieval and consolidation in neural networks. Finally, we demonstrate how regularization strategies such as elastic weight consolidation are more well-suited for larger neural networks whereas rehearsal strategies such as gradient episodic memory are better suited for smaller neural networks.

Individual and contextual factors have a profound impact on an individual's creativity. In the first part of this research, we concluded that, for a programmer's creativity intention, individual factors including big 5 personality traits and knowledge collection behaviour play a key role. However, it is important to bring contextual factors into the model to provide a holistic understanding. Hence, the objective of the present research is to expand the earlier work by (i) identifying the software engineering occupational stressors relevant to programmers, and (ii) examining their impact as moderators for the relationship between individual factors (i.e., big five personality traits and knowledge collection behaviour) and the creativity intention of the programmer. To analyse the moderating impact of 6 stressors, the survey questionnaire was used to collect data from 294 programmers working in software companies in Pakistan. The data were analysed using the Structural Equation Modelling (SEM) – Partial Least Square (PLS) technique. The findings revealed that in the presence of a moderate level of stress, the relationship between knowledge collection behaviour and creativity intention was strengthened. Furthermore, stressors interacted differently with different personality traits. An overarching statement could be that most of the stressors positively moderated the relationships between different personality traits and creativity intentions. However, contrary to the prior research, the majority of the stressors negatively affected the impact of the openness to experience trait on creativity intention. The research significantly contributes to the body of knowledge of behavioural software engineering. The findings of this research are novel and intriguing in many aspects and will benefit software organizations to increase innovation, by increasing programmers’ creativity through mitigating stress. The study is also one of the few studies which have attempted to understand the interaction between individual and contextual factors with a programmer's creativity.

Software testing is one of the crucial supporting processes of the software life cycle. Unfortunately for the software industry, the role is stigmatized, partly due to misperception and partly due to treatment of the role. The present study aims to analyse the situation to explore what restricts computer science and software engineering students from taking up a testing career in the software industry. To conduct this study, we surveyed 88 Pakistani students taking computer science or software engineering degrees. The results showed that the present study supports previous work into the unpopularity of testing compared to other software life cycle roles. Furthermore, the findings of our study showed that the role of tester has become a social role, with as many social connotations as technical implications.

Transfer learning techniques have been proved to be effective in the field of Cross-project defect prediction (CPDP). However, some questions still remain. First, the conditional distribution difference between source and target projects has not been considered. Second, facing multiple source projects, most studies only rarely consider the issues of source selection and multi-source data utilization; instead, they use all available projects and merge multi-source data together to obtain one final dataset. To address these issues, in this paper, we propose a three-stage weighting framework for multi-source transfer learning (3SW-MSTL) in CPDP. In stage 1, a source selection strategy is needed to select a suitable number of source projects from all available projects. In stage 2, a transfer technique is applied to minimize marginal differences. In stage 3, a multi-source data utilization scheme that uses conditional distribution information is needed to help guide researchers in the use of multi-source transferred data. First, we have designed five source selection strategies and four multi-source utilization schemes and chosen the best one to be used in stage 1 and 3 in 3SW-MSTL by comparing their influences on prediction performance. Second, to validate the performance of 3SW-MSTL, we compared it with four multi-source and six single-source CPDP methods, a baseline within-project defect prediction (WPDP) method, and two unsupervised methods on the data from 30 widely used open-source projects. Through experiments, bellwether and weighted vote are separately chosen as a source selection strategy and a multi-source utilization scheme used in 3SW-MSTL. And, our results indicate that 3SW-MSTL outperforms four multi-source, six single-source CPDP methods and two unsupervised methods. And, 3SW-MSTL is comparable to the WPDP method. The proposed 3SW-MSTL model is more effective for considering the two issues mentioned before.

Abstract To improve and sustain the quality of software products, software process improvement (SPI) is needed. Currently, small and medium software enterprises (SMSEs) represent a high proportion of companies around the world and become a cornerstone in the worldwide industry economy. These companies have realized that improving their process is crucial for success, but they are facing difficulties to implement it due to limited resources, limited knowledge, and time constraints. This study aimed to identify the sustainability success factors (SSFs) that have a positive impact on implementing SPI efforts in SMSEs. Data were collected through a systematic literature review (SLR) approach and quantitatively through a survey questionnaire. A list of 44 SSFs was identified during SLR and empirical study. Results illustrate that there is a positive correlation between the ranks obtained from both dataset (rs (44) = .548, ρ = .001). Therefore, there would be significant differences between the SSFs identified in both datasets. In conclusion, the top‐ranked factors can then be used to guide the SPI coordinators on where they should focus their attention to reach the desired SPI goals, which is crucial to deliver the software products and also facilitates in development of model for SPIs in the future.

The design of recommendation systems is based on complex information processing and big data interaction. This personalized view has evolved into a hot area in the past decade, where applications might have been proved to help for solving problem in the software development field. Therefore, with the evolvement of Recommendation System in Software Engineering (RSSE), the coordination of software projects with their stakeholders is improving. This experiment examines four open source recommender systems and implemented a customized recommender engine with two industrial-oriented packages: Lenskit and Mahout. Each of the main functions was examined and issues were identified during the experiment.

Over the past two decades, there has been a growing interest in modeling the elements that need to be considered when assigning people to roles in software projects, as evidenced by the number of available publications related to the topic. However, for the most part, these studies, have taken only a partial approach to the issue. Some have focused on the target role’s competency profile, while others have tried to understand the preferences of software developers for activities linked to certain roles and the relationship between these preferences and the candidate’s personal traits, to mention only two examples. Our research aims to find elements that can be integrated into an allocation model that complements current approaches by including competencies, personal traits, and project team building theories. To do so we performed an expert’s consultation exercise using the DELPHI method; which allowed us to validate a set of patterns related to different candidate’s personal traits, and the link between the team’s motivational motors and their roles within the software development.

Automated Driving System refers to a vehicle system where hardware and software are collectively capable of on-road operational and tactical functions and such functions involve the detection, recognition, classification of objects and response to events. Many automotive companies are incorporating automated driving into their current R&D and are transforming their business models. To both conventional and disruptive manufacturers, safety is always one of the top priorities. Appropriate verification and validation procedures are needed and should be followed to mitigate unreliability and hazardousness. Sufficient testing scenario should be considered and planned to simulate and cover functional and non-functional requirements. Disengagement ratio serves as an indicator during performance evaluations because analysing root causes of both technical and non-technical disengagements is pivotal especially during testing strategy planning. Autonomous Vehicle Disengagement Reports and Autonomous Vehicle Collision Reports from the Department of Motor Vehicles (DMV), California, USA are collectively used for the purpose of this research. And the analytical result shows there is no clear relationship between mileage and disengagements. Influencing factors are generated and consolidated from the mentioned reports and are proposed in addition to a Society of Automotive Engineers International (SAE International) standard. Stakeholders will benefit from the presented rationales and consider the suggestive parameters throughout their developing and testing activities. This paper further recommends testing management for automated driving systems, especially test driver management and test routes planning. And the recommendations are in accordance with the analytical results and feedback from KPMG s Global Automotive Executive Surveys.

As software systems are becoming more pervasive, they are also becoming more susceptible to failures, resulting in potentially lethal combinations. Software testing is critical to preventing software failures but is, arguably, the least understood part of the software life cycle and the toughest to perform correctly. Adequate research has been carried out in both the process and technology dimensions of testing, but not in the human dimensions. This paper attempts to fill in the gap by exploring the human dimension, i.e., trying to understand the motivation of software professionals to take up and sustain testing careers. Towards that end, a survey was conducted in four countries - India, Canada, Cuba, and China - to try to understand how professional software testers perceive and value work-related factors that could influence their motivation to take up and sustain testing careers. With a sample of 220 software professionals, we observed that very few professionals are keen to take up testing careers. Some aspects of software testing, such as the learning opportunities, appear to be a common motivator across the four countries; whereas the treatment meted out to testers as second-class citizens and the complexity of the job appeared to be the most important de-motivators. This comparative study offers useful insights that can help global software industry leaders to come up with an action plan to put the software testing profession under a new light. That could increase the number of software engineers choosing testing careers, which would facilitate quality testing.

Context. Over the last several decades, computer games started to have a significant impact on society. However, although a computer game is a type of software, the process to conceptualize, produce and deliver a game could involve unusual features. In software testing, for instance, studies demonstrated the hesitance of professionals to use automated testing techniques with games, due to the constant changes in requirements and design, and pointed out the need for creating testing tools that take into account the flexibility required for the game development process. Goal. This study aims to improve the current body of knowledge regarding these theme and point out the existing particularities observed in software testing considering the development of a computer game. Method. A mixed-method approach based on a case study and a survey was applied to collect quantitative and qualitative data from practitioners regarding the particularities of software testing in game development. Results. We analyzed over 70 messages posted on three well-established network of question-and-answer communities and received answers of 38 practitioners, and identified important aspects to be observed in the process of planning, performing and reporting tests games. Conclusion. Considering computer games, software testing must focus not only on the common aspects of a general software, but also, track and investigate issues that could be related to game balance, game physics and entertainment related-aspects to guarantee the quality of computer games and a successful testing process.

Abstract Ineffective software team composition has become recognized as a prominent aspect of software project failures. Reports from results extracted from different theoretical personality models have produced contradicting fits, validity challenges, and missing guidance during software development personnel selection. It is also believed that the technique/s used while developing a model can impact the overall results. Thus, this study aims to (1) discover an effective classification technique to solve the problem and (2) develop a model for composition of the software development team. The model developed was composed of 3 predictors: team role, personality types, and gender variables; it also contained 1 outcome: team performance variable. The techniques used for model development were logistic regression, decision tree, and rough sets theory (RST). Higher prediction accuracy and reduced pattern complexity were the 2 parameters for selecting the effective technique. Based on the results, the Johnson algorithm (JA) of RST appeared to be an effective technique for a team composition model. The study has proposed a set of 24 decision rules for finding effective team members. These rules involve gender classification to highlight the appropriate personality profile for software developers. In the end, this study concludes that selecting an appropriate classification technique is one of the most important factors in developing effective models.

Abstract An individual's decision‐making behavior is heavily influenced by and adapted to external environmental factors. Given that software development is a human‐centered activity, individual decision‐making behavior may affect the software project quality. Although environmental factors affecting decision‐making behavior in software projects have been identified in prior literature, there is not yet an objective and a full taxonomy of these factors. Thus, it is not trivial to manage these complex and diverse factors. To address this deficiency, we first design a semantic similarity algorithm between words by utilizing the synonymy and hypernymy relationships in WordNet. Further, we propose a method to measure semantic similarity between phrases and apply it into k‐means clustering algorithm to group these factors. Subsequently, we obtain a taxonomy of the environmental factors affecting individual decision‐making behavior in software projects, which includes 11 broad categories, each containing 2 to 5 sub‐categories. The taxonomy presented herein is obtained by an objective method, and quite comprehensive, with appropriate references provided. The taxonomy holds significant value for researchers and practitioners; it can help them to better understand the major aspects of environmental factors, also to predict and guide the behavior of individuals during decision making towards a successful completion of software projects.

Context. Considering the importance of software testing to the development of high quality and reliable software systems, this paper aims to investigate how can work-related factors influence the motivation of software testers. Method. We applied a questionnaire that was developed using a previous theory of motivation and satisfaction of software engineers to conduct a survey-based study to explore and understand how professional software testers perceive and value work-related factors that could influence their motivation at work. Results. With a sample of 80 software testers we observed that software testers are strongly motivated by variety of work, creative tasks, recognition for their work, and activities that allow them to acquire new knowledge, but in general the social impact of this activity has low influence on their motivation. Conclusion. This study discusses the difference of opinions among software testers, regarding work-related factors that could impact their motivation, which can be relevant for managers and leaders in software engineering practice.

Context: In the workplace, some individuals engage in the voluntary and intentional generation, promotion, and realization of new ideas for the benefit of individual performance, group effectiveness, or the organization. The literature classifies this phenomenon as innovative behaviour. Despite its importance to the development of innovation, innovative behaviour has not been fully investigated in software engineering. Objective: To understand the factors that support or inhibit innovative behaviour in software engineering practice. Method: We conducted a pilot case study in a Canadian software company using interviews and observations as data collection techniques. Using qualitative analysis, we identified relevant factors and relationships not addressed by studies from other areas. Results: Individual innovative behaviour is influenced by individual attitudes and also by situational factors such as relationships in the workplace, organizational characteristics, and project type. We built a model to express the interacting effects of these factors. Conclusions: Innovative behaviour is dependent on individual and contextual factors. Our results contribute to relevant impacts on research and practice, and to topics that deserve further study.

Context: A case study is a powerful research strategy for investigating complex social-technical and managerial phenomena in real life settings. However, when the phenomenon has not been fully discovered or understood, pilot case studies are important to refine the research problem, the research variables, and the case study design before launching a full-scale investigation. The role of pilot case studies has not been fully addressed in empirical software engineering research literature. Objective: To explore the use of pilot case studies in the design of full-scale case studies, and to report the main lessons learned from an industrial pilot study. Method: We designed and conducted an exploratory case study to identify new relevant research variables that influence the innovative behaviour of software engineers in the industrial setting and to refine the full-scale case study design for the next phase of our research. Results: The use of a pilot case study identified several important research variables that were missing in the initial framework. The pilot study also supported a more sophisticated case study design, which was used to guide a full-scale study. Conclusions: When a research topic is has not been fully discovered or understood, it is difficult to create a case study design that covers the relevant research variables and their potential relationships. Conducting a full-scale case study using an untested case design can lead to waste of resources and time if the design has to be reworked during the study. In these situations, the use of pilot case studies can significantly improve the case study design.

Context: The increase in the number of qualitative and mixed-methods research published in software engineering has created an opportunity for further knowledge generation through the synthesis of studies with similar aims. This is particularly true in the research on human aspects because the phenomena of interest are often better understood using qualitative research. However, the use of qualitative synthesis methods is not widespread and worked examples of their consistent application in software engineering are needed. Objective: To explore the use of meta-ethnography in the synthesis of empirical studies in software engineering through an example using studies about the relations between personality and software team processes. Methods: We applied the seven phases of meta-ethnography on a set of articles selected from a previously developed systematic review, to assess the appropriateness of meta-ethnography in this domain with respect to ease of use, and usefulness and reliability of results. Results: Common concepts were identified through reading and interpreting the studies. Then, second order translations were built and used to synthesize a model of the relationships between personality and software team processes. Conclusions: Meta-ethnography is adequate in the synthesis of empirical studies even in the context of mixed-methods studies. However, we believe that the method should not be used to synthesize studies that are too disparate to avoid the development of gross generalizations, which tend to be fruitless and are contrary to the central tenets of interpretive research.

Smart phones have become the most used electronic devices. They carry out most of the functionalities of desktops, offering various useful applications that suit the user's needs. Therefore, instead of being an operator, the user has become the main controller of the device and its applications. Therefore, its reliability becomes an emergent requirement. In this paper, we shortly highlight the shortcomings of Software Reliability Growth Models (SRGMs) when applied to Smartphone applications and suggest future directions for reliability prediction in the mobile area.

A software development project is considered to be risky due to the uncertainty of the information (customer requirements), the complexity of the process, and the intangible nature of the product. Under these conditions, risk management in software development projects is mandatory, but often it is difficult and expensive to implement. Expert COCOMO is an efficient approach to software project risk management, which leverages existing knowledge and expertise from previous effort estimation activities to assess the risks in new software projects. However, the original method has limitation because it cannot effectively deal with imprecise and uncertain inputs in the form of linguistic terms such as: Very Low (VL), Low (L), Nominal (N), High (H), Very High (VH) and Extra High (XH). This paper introduces the fuzzy-ExCOM methodology that combines the advantages of a fuzzy technique with Expert COCOMO methodology for risk assessment in software projects. The validation of this approach with industrial data shows that fuzzy-ExCOM provides better risk assessment results with a higher level of sensitivity with respect to risk identification compared to the original Expert COCOMO methodology.

In this paper, we propose a novel Artificial Neural Network (ANN) to predict software effort from use case diagrams based on the Use Case Point (UCP) model. The inputs of this model are software size, productivity and complexity, while the output is the predicted software effort. A multiple linear regression model with three independent variables (same inputs of the ANN) and one dependent variable (effort) is also introduced. Our data repository contains 240 data points in which, 214 are industrial and 26 are educational projects. Both the regression and ANN models were trained using 168 data points and tested using 72 data points. The ANN model was evaluated using the MMER and PRED criteria against the regression model, as well as the UCP model that estimates effort from use cases. Results show that the ANN model is a competitive model with respect to other regression models and can be used as an alternative to predict software effort based on the UCP method.

Software effort prediction is an important task in the software development life cycle. Many models including regression models, machine learning models, algorithmic models, expert judgment and estimation by analogy have been widely used to estimate software effort and cost. In this work, a Tree boost (Stochastic Gradient Boosting) model is put forward to predict software effort based on the Use Case Point method. The inputs of the model include software size in use case points, productivity and complexity. A multiple linear regression model was created and the Tree boost model was evaluated against the multiple linear regression model, as well as the use case point model by using four performance criteria: MMRE, PRED, MdMRE and MSE. Experiments show that the Tree boost model can be used with promising results to estimate software effort.

Software cost estimation is a crucial element in project management. Failing to use a proper cost estimation method might lead to project failures. According to the Standish Chaos Report, 65% of software projects are delivered over budget or after the delivery deadline. Conducting software cost estimation in the early stages of the software life cycle is important and this would be helpful to project managers to bid on projects. In this paper, we propose a novel model to predict software effort from use case diagrams using a cascade correlation neural network approach. The proposed model was evaluated based on the MMER and PRED criteria using 214 industrial and 26 educational projects against a multiple linear regression model and the Use Case Point model. The results show that the proposed cascade correlation neural network can be used with promising results as an alternative approach to predict software effort.

Software effort estimation is one of the most important tasks in software engineering. Software developers conduct software estimation in the early stages of the software life cycle to derive the required cost and schedule for a project. In the requirements stage, where most software estimation is conducted, the available information is usually imprecise or incomplete. In this paper, a new regression model is created for software effort estimation based on use case point model. Furthermore, a Sugeno Fuzzy Inference System (FIS) approach is applied on this model to improve the estimation. Results show that an improvement of 11% can be achieved in MMRE after applying the Sugeno fuzzy logic approach.

Software product line engineering is an inter-disciplinary concept. It spans over the dimensions of business, architecture, process and organization. Some of the potential benefits of this approach include cost reduction, improvement in quality and a decrease in product development time. Employees' participation, organizational behavior and management contemplation play a vital role in successfully institutionalizing software product lines in a firm. Organizational dimension has been weighted as one of the critical dimensions in software product line theory and practice. Organizational behavior covers organizational culture, organizational commitment and organizational learning. A comprehensive empirical investigation to study the relationship of some organizational behavior on the performance of software product line practice is presented in this work. The results of this investigation provide empirical evidence and further support the theoretical foundations that in order to institutionalize software product lines within an organization, organizational behavior play an important role.

Software product line (SPL) and open source software (OSS) have emerged as successful modes of developing software. There is an increased interest in developing suitable approaches for combining the promised advantages of SPL and OSS. Researchers and practitioners have been emphasizing the need of exploiting the ever growing repositories of OSS components for developing SPLs. However, there is no conceptual model for guiding the process of developing OSS-based SPLs. In this paper, we propose a model for developing software product line using open source software. This paper identifies and elaborates the essential phases and activities of the proposed model of developing OSS-based SPLs. This model emphasizes the vital role of software architecture and asserts that software architectures of OSS can be exploited to establish a SPL. To demonstrate this, we have evaluated Eclipse's architecture for its potential to support different flavors of a system.

Software size estimation is an important aspect in software development projects because poor estimations can lead to late delivery, cost overruns and possibly project failure. Backfiring is a popular technique for sizing and predicting the volume of source code by converting the function point metric into source lines of code mathematically using conversion ratios. While this technique is popular and useful, there is a high margin of error in backfiring. This research introduces a new method to reduce this margin of error. Neural networks and fuzzy logic in software prediction models have been demonstrated in the past to have improved performance over traditional techniques. For this reason, a neuro-fuzzy approach is introduced to the backfiring technique to calibrate the conversion ratios. This paper presents the neuro-fuzzy calibration solution and compares the calibrated model against the default conversion ratios currently used by software practitioners.

Software testing is a very expensive and time consuming process. It can account for up to 50% of the total cost of the software development. Distributed systems make software testing a daunting task. The research described in this paper investigates a novel multi-agent framework for testing 3-tier distributed systems. This paper describes the framework architecture as well as the communication mechanism among agents in the architecture. Web-based application is examined as a case study to validate the proposed framework. The framework is considered as a step forward to automate testing for distributed systems in order to enhance their reliability within an acceptable range of cost and time.

Accurate software estimation such as cost estimation, quality estimation and risk analysis is a major issue in software project management. We present a soft computing framework to tackle this challenging problem. We first use a preprocessing neuro-fuzzy inference system to handle the dependencies among contributing factors and decouple the effects of the contributing factors into individuals. Then we use a neuro-fuzzy bank to calibrate the parameters of contributing factors. In order to extend our framework into fields that lack of an appropriate algorithmic model of their own, we propose a default algorithmic model that can be replaced when a better model is available. Validation using industry project data shows that the framework produces good results when used to predict software cost.

In this research work object-oriented concepts are applied to a software maintenance method named COMFORM (configuration management formalization for maintenance). COMFORM is composed of several phases to provide the necessary guidance to maintain existing software systems. One of the aims of the method is redocumentation by keeping the maintenance history and information related to the software modules being maintained. Redocumentation is obtained by filling in pre-defined forms which go hand in hand with the phases of the method. Each form is mapped by a class definition. The concepts of classes and inheritance are used along with software maintenance to help the system manager in the creation and administration of such forms. Version control of the predefined forms is carried out either by reusing common parts of these forms or by defining new subclasses from them. As a consequence, a generic implementation of the method is achieved, thus maintenance support for a wide range of existing software systems with various profiles is accomplished.>