Summarization techniques for code, change, testing, and user feedback (Invited paper)

Feature location aims at locating pieces of code that implement a given set of features (requirements). It is a necessary first step in every program comprehension and maintenance task if the connection between features and code has been lost.We have developed a semi-automatic technique for feature location using a combination of static and dynamic program analysis. Formal concept analysis is used to explore the results of the dynamic analysis.We describe new experiences with our technique. Specifically, we investigate the gain of information and increase of costs when the system under analysis is profiled at basic block level rather than routine level as in our earlier work. Furthermore, we explore the influence of the scenarios used for the dynamic analysis (minimal versus combined scenarios).

Society becomes more dependent on software, and the quality of software has a direct and vital impact on the life of people. Therefore, the importance of software engineering ethics is highlighted. Further, it is necessary to increase the emphasis on ethics education in software engineering curricula. However, in practice, there is generally not a single software engineering ethics course. Even if there is, it is a marginal role. How to effectively integrate ethics topics into software engineering education is a key issue. In this paper, we first analyze the relevance of two domains: Software Engineering Code of Ethics and Professional Practice and software engineering management that is a subdomain of Software Engineering Body of Knowledge. The results show that they are highly related. Therefore, it is feasible and reasonable to integrate ethics issues into the course of software engineering management, which is usually a required course. This integration brings a solid position for the ethical education. In addition, we suggest new description of codes to promote their fusion, further put forward some ideas about their fusion in education practice. Our contribution is to put forward a new idea for the ethics education in software engineering field.

An empirical study of software maintenance tasks was carried out in a large Norwegian organization in the period 1992–93. More than one hundred randomly selected maintenance tasks were studied through interviews with the maintainers performing the tasks immediately before they started the tasks and immediately after they had completed the tasks. The collected data is used to develop distributions and to test 33 hypotheses about software maintenance. The findings from the study indicate, for example, that Application system documentation plays only a minor role as maintenance information source. The maintenance productivity, measured in LOC/effort, is predicted by the size of the task and type of change in the source code, but seems rather independent of language level, maintainer experience, application age and application size. There is an economy of scale for software maintenance tasks. The type of programming language does not correlate with proportion of unexpected maintenance problems, task complexity or task size. The proportion of corrective maintenance decreases with increasing age of the application.

A software is developed to satisfy some requirements that could change with respect to technology progress or new clients needs. That is why a software evolves continually in order to fit with occurring requirements. Software evolution or maintenance represents a non-trivial issue that should be treated seriously. In fact, according to some studies software maintenance could consume about 80% of software development total cost. In most of works, such issue was treated in a source code level by proposing new techniques and tools that could optimize maintenance cost. In this paper, we treat the same issue but in an earlier phase of software development and exactly in software design. We propose here an UML profile that allows to software developers and designers to carry about maintenance tasks at the design phase in order to predict handling maintenance tasks before happening, to get an historical trace of these tasks and to express explicitly on models some design patterns helping to resolve software maintenance problems in order to make maintenance easier and cheaper.

With a great number of software applications that have been developed, software maintenance has become an important and challenging task, particularly due to the increasing scale of software projects. Even if developers can create and update bug reports in bug repositories to support software maintenance, a large software project receives a large number of bug reports each day. For reducing the workload of developers, many researchers and software engineers have begun recommending appropriate developers to fix bugs. This process is called bug triage and is a hot research topic for software maintenance. In this paper, we propose a hybrid bug triage algorithm, combining a probability model and an experience model to rank all candidate developers for fixing a new bug. For this study, we adopted the smoothed Unigram Model (UM) instead of the traditional Vector Space Model (VSM) to search similar bug reports. In the probability model, we used a social network to analyze the probability of fixing a new bug for a candidate developer. We first proposed to add a new feature (the number of re-opened bugs) in order to get the fixing probability. In the experience model, we considered the number of fixed bugs and fixing cost for each candidate developer as the estimate factor. In addition, we introduced a new concept, activity factor, to better model developers' experience. We performed the experiments on two large-scale, open source projects. The results show that our method can effectively recommend the best developer for fixing bugs.

Software quality assurance is concerned with the efficient and effective development of large, reliable, and high-quality software systems. In agile software development and maintenance, refactoring is an important phase for the continuous improvement of a software system by removing quality defects like code smells. As time is a crucial factor in agile development, not all quality defects can be removed in one refactoring phase (especially in one iteration). Documentation of quality defects that are found during automated or manual discovery activities (e.g., pair programming) is necessary to avoid wasting time by rediscovering them in later phases. Unfortunately, the documentation and handling of existing quality defects and refactoring activities is a common problem in software maintenance. To recall the rationales why changes were carried out, information has to be extracted from either proprietary documentations or software versioning systems. In this chapter, we describe a process for the recurring and sustainable discovery, handling, and treatment of quality defects in software systems. An annotation language is presented that is used to store information about quality defects found in source code and that represents the defect and treatment history of a part of a software system. The process and annotation language can not only be used to support quality defect discovery processes, but is also applicable in testing and inspection processes.

Search-based software engineering (SBSE) consists of the application of metaheuristic search techniques—such as genetic algorithms, simulated annealing, hill climbing, and tabu search—to software engineering problems. This idea is based on the observation that some software engineering activities can be thought of as optimization problems, and on the fact that techniques providing exact solutions are, very often, unable to adequately scale to large, real-world software engineering problems. Define fitness functions to guide an automated search for solutions, rather than attempting to construct solutions from scratch by hand. SBSE moves the engineer up the abstraction chain, from the tedious and expensive role of detailed solution construction, to the potentially more productive and more cost-effective area of fitness function design. This approach has been repeatedly demonstrated to have successful applications across the software engineering life cycle from requirements and project planning to maintenance and testing. This special issue focuses on applications of search SBSE to the maintenance phase of the life cycle. After software testing 2, software maintenance is the most widely studied application area for SBSE research. Work on SBSE applications to problems in software maintenance can be traced back to the pioneering work of the Mancoridis et al. 3, which led to the development of the Bunch tool for software re-modularization 4. In the past five years there has been a significant increase in SBSE applications to software maintenance and evolution. Search-based techniques have been applied to post-development code improvement problems, such as object-oriented refactoring 5, 6, and imperative language slicing and transformation 7. Other applications are related to software engineering economics and project management problems, such as release planning 8-12, effort, and cost estimation 13-16, and project staffing and planning 17-20. Last, but not the least, SBSE has been applied to deal with service-oriented architectures, for example for services QoS-aware composition 21, service-level agreement negotiation 22, and testing 23. A complete overview of SBSE with a discussion of future research trends can be found in the recent International Conference on Software Engineering (ICSE) ‘Future of Software Engineering’ paper 24. This issue of the Journal of Software Maintenance and Evolution: Research and Practice features three outstanding papers, dealing with different aspects of software maintenance and evolution: release planning and staffing, re-factoring, and component substitution. The paper ‘Optimized Staffing for Product Releases—Focused Search and its Application at Chartwell Technologies’ by Puneet Kapur, An Ngo-The, Günther Ruhe, and Andrew Smith presents an approach for optimizing the staffing for product releases. The problem they solve can be viewed as part of the more general release planning problem, which assigns features to releases such that technical, resource, risk, and budget constraints are met. In particular, the genetic search-based approach the authors propose aims at assigning human resources to software development tasks, taking them from a pool of developers having different levels of skills. The applicability of the proposed approach is demonstrated with an industrial case study. The paper ‘Search-Based Refactoring: an Empirical Study’ by Mark O'Keffe and Mel Ó Cinnéide presents an approach to automatically re-factor, using search-based techniques, object-oriented systems that undergo repeated addition of functionality. Such addition can, very often, deteriorate the quality of the system's underlying design. Their re-factoring is guided by the QMOOD quality model 25, and the paper compares different optimization techniques, such as Genetic Algorithms, Simulated Annealing, and Multiple Ascent Hill-Climbing. The results of experiments carried out on five software systems suggest that the latter technique outperforms the others. The paper ‘Search-based Many-to-one Component Substitution’ by Nicolas Desnos, Marianne Huchard, Guy Tremblay, Christelle Urtado, and Sylvain Vauttier presents a search-based approach that allows the replacement of an obsolete component with a composition of multiple components. When a component becomes obsolete, the approach searches for a minimal assembly of other components, exposing their functionality by means of ports that preserves the behavior of the original component. The proposed approach has been evaluated on a set of randomly generated components. We hope that readers will enjoy this special issue and find the selected papers insightful and inspiring. We would like to thank all the authors who presented papers for this special issue: there were many good quality papers, but unfortunately we could accept only three of them. Last, but not the least, we would like to thank the reviewers who provided timely, detailed and constructive feedbacks to the authors. Without their precious help this special issue would not have been possible.

In industry, software projects might span over decades, with many engineers joining or leaving the company over time. In these circumstances, no single engineer has all of the knowledge when maintenance tasks such as Traceability Link Recovery (TLR), Bug Localization (BL), and Feature Location (FL) are performed. Thus, collaboration has the potential to boost the quality of maintenance tasks since the solution advanced by one engineer might be enhanced with contributions from other engineers. However, assembling a team of software engineers to collaborate may not be as intuitive as we might think. In the context of a worldwide industrial supplier of railway solutions, this work evaluates how the quality of TLR, BL, and FL is affected by the criteria for selecting engineers for collaboration. The criteria for collaboration are based on engineers’ profile information to select the set of search queries that are involved in the maintenance task. Collaboration is achieved by applying automatic query reformulation, and the location relies on an evolutionary algorithm. Our work uncovers how software engineers who might be seen as not being relevant in the collaboration can lead to significantly better results. A focus group confirmed the relevance of the findings.

Multi-attribute decision making (MADM) is a state-of-the-art, popular technique for dealing with real-world problems. An effective decision can be made for various real-world problems involving multiple attributes to decide a proper solution. Software testing and bug fixing are essential steps in the field of software engineering. Bug triaging is a real challenge in large-scale software development. Bug triaging is the process of allocating newly reported bugs to the best developer who meets the requirements for addressing them. In this paper, a software developer ranking model based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is proposed for an effective bug triaging process. Developers are ranked based on various criteria using the TOPSIS model for effective bug triaging. Assigning newly reported bugs to the appropriate and available software developer is a complex decision-making process. It involves consideration of multiple criteria for discovering the optimal solution. In software engineering, bug triaging refers to the process of allocating appropriate developers to a newly reported bug. This paper presents criteria for finding suitable developers using multi-criteria decision-making (MCDM) techniques. The Analytic Hierarchy Process (AHP) method is used to determine the weights of the criteria, and the TOPSIS MCDM technique is used to rank the most appropriate developer.

Automatic and semi-automatic approaches supporting program comprehension are sought by researchers and practitioners to facilitate software engineering tasks, such as development, maintenance, extension and so on. Using topic modeling is a promising way to automatically discover feature and structure from textual software assets. However, there are gaps between knowing and doing when applying topic modeling to software engineering practice. In this paper, we explored how to infuse topic modeling into understanding Java programs in a generic level, and summarized the whole procedure as a methodology called MAT. MAT utilizes essential information automatically generated from Java projects to establish a project overview and to bring search capability for software engineers. Experiments on two open source Java projects suggest that MAT can support program comprehension for Java software engineers during carrying on software maintenance and extension tasks.

Software defect detection is a critical research topic in the field of software engineering, aiming to identify potential defects during the development process to improve software quality and reduce maintenance costs. This study proposes a novel feature selection and defect prediction classification algorithm based on a multi-strategy enhanced Parrot Optimization (PO) algorithm. Firstly, to address the limitations of the original Parrot Optimization algorithm, such as strong dependence on the initial population, premature convergence, and insufficient global search capability, this article develops a multi-strategy enhanced Parrot Optimization algorithm (MEPO). Experiments conducted on eight benchmark test functions validate the superior performance of MEPO in terms of convergence speed and solution accuracy. Secondly, to mitigate the adverse impact of irrelevant features on model performance in traditional software defect prediction methods, this study introduces a binary multi-strategy enhanced Parrot Optimization algorithm (BMEPO) for optimizing feature selection. Comparative experiments demonstrate that BMEPO exhibits stronger competitiveness in feature selection quality and classification performance compared to advanced feature selection algorithms. Finally, to further enhance the classification performance of defect prediction, a heterogeneous data stacking ensemble learning algorithm (HEDSE) based on feature selection is proposed. Experimental evaluations on 16 open-source software defect datasets indicate that the proposed HEDSE outperforms existing methods, providing a novel and effective solution for software defect prediction. The proposed approaches in this study hold significant practical value, particularly in improving software quality, optimizing testing resource allocation, and reducing maintenance costs, offering broad potential for application in real-world software engineering scenarios.

Software Engineering is an application of engineering to the design, development, functioning and maintenance of software in a systematic manner. This paper deals with a model for the extraction of documents based on key topic search by automatically parsing the software engineering papers that are published so far. The performance of the model is evaluated based on the key topic search in the software engineering field. Using text mining technique such as Natural Language processing (NLP, the semantics of the text from which the importance of a term in a sentence of document can be captured. The statistical approaches of NLP, Log Likelihood and RV Coefficient are used for extraction of documents based on the key features. The publications are extracted and clustered with similar papers From the experimental analysis given its clear that the proposed technology using Log Likelihood comparison and RV coefficient is efficient in finding out the most important topics rather than by using the existing technologies such as TF-IDF.

Although Scrum is an important topic in software engineering and information systems, few longitudinal industrial studies have investigated the effects of Scrum on software quality, in terms of defects and defect density, and the quality assurance process. In this paper we report on a longitudinal study in which we have followed a project over a three-year period. We compared software quality assurance processes and software defects of the project between a 17-month phase with a plan-driven process, followed by a 20-month phase with Scrum. The results of the study did not show a significant reduction of defect densities or changes of defect profiles after Scrum was used. However, the iterative nature of Scrum resulted in constant system and acceptance testing and related defect fixing, which made the development process more efficient in terms of fewer surprises and better control of software quality and release date. In addition, software quality and knowledge sharing got more focus when using Scrum. However, Scrum put more stress and time pressure on the developers, and made them reluctant to perform certain tasks for later maintenance, such as refactoring.

With the enhancement of hardware and software engineering, the effectiveness and correctness of software is less and less doubted and customers are more aware about whether software services are available or not when needed. Software maintenance is one of the main reasons that make software unavailable and it is often very expensive to perform maintenance tasks. Common approaches of studying software maintenance are to consider it as a static by‐product of software operation and only the maintenance cost is covered. In this paper, software maintenance policies are studied with the consideration of unavailable service time. A non‐homogeneous continuous Markov chain is adopted for modeling the software operation and maintenance process, and the cost of software unavailability that is brought in by software maintenance is investigated and analyzed for searching the optimal maintenance policy, which aims at minimizing the average maintenance time cost. The optimality of our proposed policy is shown and checked by numerical examples with discussions of its possible application perspectives. Copyright © 2010 John Wiley & Sons, Ltd.

(ProQuest: ... denotes formulae omitted.)1 Research Premises and HypothesisMaintenance costs have a consistent weight in the total costs generated by an application during its entire lifecycle. Previously conducted research and studies that focus on software maintenance support this claim. In [2], the 60/60 rule of software is depicted. The rule claims that maintenance accounts for 60% of the total costs generated by a software in its entire lifecycle. Enhancement or perfective [3] maintenance tasks account for 60% of all the costs generated by maintenance [2], whereas error correction or corrective [3] maintenance tasks account only for 17% of total maintenance costs [2]. In [4] it's stated that software applications evolve over time to meet the changing needs which leads to maintenance generating 67% of the applications total lifecycle costs. In [5] it is argued that maintenance typically accounts for 75% or more of the total software workload. Other relevant research place maintenance costs at 90% [6], 75% [7], 90% [8] or at 60% to 70% [9] of the overall application's lifecycle cost. It is therefore of paramount importance to accurately estimate maintenance cost before implementing a software project in order to better comprehend the total costs associated with developing and owing a software application. Better understanding maintenance costs will provide valuable information regarding resource planning and the appropriate time to decommission the application.The cost of maintenance is directly determined by the number of people involved in the maintenance process and the number of hours each person invests in the maintenance tasks. Thus in order to estimate maintenance cost one needs to estimate the effort invested in the maintenance process. The maintenance effort is expressed in man-hours which entails the number of ours dedicated by the maintenance team to implementing maintenance tasks. Maintenance in web applications has two main components: content maintenance and technical maintenance.Content maintenance requires performing tasks of a non-technical nature like updating product inventory and information, adding news or articles, managing blog posts, updating contact or brand and visual identity information and moderating user comments or messages. Content maintenance team staffing and therefore content maintenance effort is determined by the web application's size [1].According to [1] and as presented in Table 1, content maintenance cost is determined by the number of hours required to keep the web application up to date in terms of content. The larger the application the more hours need to be invested in keeping it up to date in terms of content. The more content changes in an application the more technical maintenance tasks are generated. Technical maintenance tasks are generated because content managers spot bugs or request new functionality to help keep the content up to date. Therefore, content update frequency is a factor that impacts maintenance effort and cost.Technical maintenance requires adding new functionality, performing regular back-ups, monitoring website outages, checking IP reputation, checking DNS configuration, test website speed, check for broken links, perform software updates if third party software was used when developing the web application, monitor traffic statistics and review search engine indexing.According to [1] and as presented in Table 2, technical team stuffing is determined by the level of complexity built into the web application. Thus, a Basic web application which is characterized by static content build mainly using front-end technologies like HTML, CSS and JavaScript will require no more than 1 person to perform maintenance tasks. A Dynamic web application which stores its content in a database and has functionality which is powered by PHP, Java, ASP.NET, Python, Ruby or any other high-level programming language will require 1 to 2 persons to perform maintenance tasks. …