Journal of Software-Evolution and Process最新文献

Risk management (RM) plays a role in project management in the software development field. As information technology (IT) systems become more essential across industries and IT projects continue to face failure rates effective project management becomes crucial. However, the utilization of methodologies in risk management is not widely considered, specifically in Malaysia. This study aims to investigate how software practitioners in Malaysia have implemented risk management and discover strategies that can enhance risk management for agile contexts. The main focus of this study is the limited integration of methodologies into risk management practices, which has created a gap within the software risk domain. Successful risk management is essential for the achievement of software projects, and the findings from this study can offer insights for software development organizations to make informed decisions and improve project outcomes. By utilizing a quantitative approach and adapted questionnaires, this comprehensive study collected data from 60 practitioners and conducted descriptive analysis to identify key risk elements that have significant potential to affect project performance. The findings highlight these risk elements that can significantly impact project success. Agile methodologies, with their emphasis on collaboration, communication within teams, and engagement with stakeholders, including top management, are instrumental in aligning project objectives, identifying potential risks, and resolving issues promptly. This study provides empirical insights into the risk management practices of agile practitioners in Malaysia, which can equip software development organizations with valuable knowledge for informed decision-making. By enhancing project outcomes and guiding future strategic actions, the findings of this study can contribute to the improvement of agile risk management in the software development industry, particularly in the Malaysian context.

Bug localization, which is used to help programmers identify the location of bugs in source code, is an essential task in software development. Researchers have already made efforts to harness the powerful deep learning (DL) techniques to automate it. However, training bug localization model is usually challenging because it requires a large quantity of data labeled with the bug's exact location, which is difficult and time-consuming to collect. By contrast, obtaining bug detection data with binary labels of whether there is a bug in the source code is much simpler. This paper proposes a WEakly supervised bug LocaLization (WELL) method, which only uses the bug detection data with binary labels to train a bug localization model. With CodeBERT finetuned on the buggy-or-not binary labeled data, WELL can address bug localization in a weakly supervised manner. The evaluations on three method-level synthetic datasets and one file-level real-world dataset show that WELL is significantly better than the existing state-of-the-art model in typical bug localization tasks such as variable misuse and other bugs.

Bug triaging is a vital process in software maintenance, involving assigning bug reports to developers in the issue tracking system. Current studies predominantly treat automatic bug triaging as a classification task, categorizing bug reports using developers as labels. However, this approach deviates from the essence of triaging, which is establishing bug–developer correlations. These correlations should be explicitly leveraged, offering a more comprehensive and promising paradigm. Our bug triaging model utilizes graph collaborative filtering (GCF), a method known for handling correlations. However, GCF encounters two challenges in bug triaging: data sparsity in bug fixing records and semantic deficiency in exploiting input data. To address them, we propose PCG, an innovative framework that integrates prototype augmentation and contrastive learning with GCF. With bug triaging modeled as predicting links on the bipartite graph of bug–developer correlations, we introduce prototype clustering-based augmentation to mitigate data sparsity and devise a semantic contrastive learning task to overcome semantic deficiency. Extensive experiments against competitive baselines validate the superiority of PCG. This work may open new avenues for investigating correlations in bug triaging and related scenarios.

Considering the increasing need for software projects, estimating software development efforts is essential and can lead to improved project delivery quality. Machine learning methods are widely used to improve the accuracy of estimation. The boosting method is an ensemble machine learning technique less used in this field. In this research, five boosting algorithms including Adaboost, Gradient boosting, XGBoost, LightGBM, and CatBoost were implemented with the hyperparameter tuning framework Optuna on the ISBSG database. The Optuna is a next-generation optimization method for automatically tuning hyperparameters of algorithms. Six evaluation criteria MMRE, MdMRE, MAE, MSE, Pred(0.25), and SA were used to evaluate the findings. The results show that the hyperparameter automatic tuning by Optuna increases the accuracy of prediction provided by all five models. When the Catboost algorithm uses Optuna to tune its hyperparameters has made the best prediction among the five algorithms studied in this research. Using Optuna, compared to the case where the algorithm uses its default settings, the highest percentage of prediction improvement was observed in the XGBoost algorithm (except for the SA criterion). Based on the criteria of MMRE, Pred(0.25), and SA, this study has a better prediction than some relatively similar articles.

Smart contracts (SC) are programs embodying certain business logic stored on a blockchain network like Ethereum. The execution of transactions on SC has a cost, measured in gas units, that depends on the low-level operations performed. Therefore, a poor choice of high-level language constructs could lead to overcharging users for their transactions. Thus, a testing process focused on possible deviations of the gas used in diverse scenarios could provide substantial global savings. This paper presents a gas-centered mutation testing approach for taking care of the gas consumed by Solidity SCs. This approach can be useful to improve the test quality to detect gas-related problems, reason about performance issues that only manifest in certain situations, and identify alternative more optimal implementations. We define and implement several mutation operators specifically designed to perturb gas consumption while preserving contract semantics in general. Our experiments using several real-world SCs show the feasibility of the technique, with some mutants reproducing meaningful differences in the consumption and exposing some gas limits not tight enough in historic transactions. Therefore, our approach is shown to be a good ally to prevent the appearance of gas-related issues and lays the groundwork for researchers seeking to improve performance testing practices.

During software evolution, decisions related to components' origin or source significantly impact the quality properties of the product and development metrics such as cost, time to market, ease of maintenance, and further evolution. Thus, such decisions should ideally be supported by evidence, i.e., using previous experiences and information from different sources, even own previous experiences. A hindering factor to such reuse of previous experiences is that these decisions are highly context-dependent and it is difficult to identify when previous experiences come from sufficiently similar contexts to be useful in a current setting. Conversely, when documenting a decision (as a decision experience), it is difficult to know which context factors will be most beneficial when reusing the experience in the future. An interview study is performed to identify a list of context factors that are perceived to be most important by practitioners when using experiences to support decision-making for component sourcing, using a specific scenario with alternative sources of experiences. We observed that the further away (from a company or an interviewee) the experience evidence is, as is the case for online experiences, the more context factors are perceived as important by practitioners to make use of the experience. Furthermore, we discuss and identify further research to make this type of decision-making more evidence-based.

Products and services are developed to realize chances on the market. Often, development teams are focusing primarily on the potential chances rather than on the corresponding risks of new products and services. However, it is possible to integrate the product and service quality risks early into the development procedures for a balanced and holistic view. The integration enables systematic mitigation and quality assurance actions to handle the risks. The article analyzes the literature related to quality risk approaches, identifies mature and generally applicable product and service risk approaches, and gives selected approach examples about its methodical evolution and how it can be integrated into the development procedures including soft-skills development such as employee awareness and systematical training within organizations. A case study in the Group IT of the Volkswagen AG is discussed.

The physics of notations (PoN) is a framework for designing cognitively effective notations. Cognitive effectiveness refers to the ease (speed and accuracy) by which model readers read models. In 2019, a PoN-Enabled notation for use case diagrams was proposed. It has been empirically proven that the cognitive effectiveness of this new notation is superior to the original use case notation. The new notation however relies in part on the use of color. The accessibility of this new design has not been validated for use by the colorblind community, or any users who need to use a grayscale version of the models created. The PoN framework was not designed to explicitly account for the colorblind community or users who can only access grayscale versions of models. Inclusivity should not be an afterthought in software engineering practice or research. To this end, this paper empirically evaluates the cognitive effectiveness of the PoN-enabled use case notation in situations where it is viewed in grayscale.