Summary Software evolution is the process of adapting, maintaining, and updating a software system. This process concentrates the most significant part of the software costs. Many works have studied software evolution and found relevant insights, such as Lehman's laws. However, there is a gap in how software systems evolve from an internal dimensions point of view. For instance, the literature has indicated how systems grow, for example, linearly, sub‐linearly, super‐linearly, or following the Pareto distribution. However, a well‐defined pattern of how this phenomenon occurs has not been established. This work aims to define a novel method to analyze and predict software evolution. We based our strategy on time series analysis, linear regression techniques, and trend tests. In this study, we applied the proposed model to investigate how the internal structure of object‐oriented software systems evolves in terms of four dimensions: coupling, inheritance hierarchy, cohesion, and class size. Applying the proposed method, we identify the functions that better explain how the analyzed dimensions evolve. Besides, we investigate how the relationship between dimension metrics behave over the systems' evolution and the set of classes existing in the systems that affect the evolution of these dimensions. We mined and analyzed data from 46 Java‐based open‐source projects. We used eight software metrics regarding the dimensions analyzed in this study. The main results of this study reveal ten software evolution properties, among them: coupling, cohesion, and inheritance evolve linearly; a relevant percentage of classes contributes to coupling and size evolution; a small percentage of classes contributes to cohesion evolution; there is no relation between the software internal dimensions' evolution. The results also indicate that our method can accurately predict how the software system will evolve in short‐term and long‐term predictions.

Software evolution is the process of developing, maintaining, and updating software systems. It is known that the software systems tend to increase their complexity and size over their evolution to meet the demands required by the users. Due to this fact, researchers have increasingly carried out studies on software evolution to understand the systems' evolution pattern and propose techniques to overcome inherent problems in software evolution. Many of these works collect data but do not make them publicly available. Many datasets on software evolution are outdated, and/or are small, and some of them do not provide time series from software metrics. We propose an extensive software evolution dataset with temporal information about open-source Java systems. To build this dataset, we proposed a methodology of four steps: selecting the systems using a criterion, extracting and measuring their releases, and generating their time series. Our dataset contains time series of 46 software metrics extracted from 46 open-source Java systems, and we make it publicly available.

This paper proposes new reliability models whose likelihood consists of decomposition of data information in stages or times, thus leading to latent state parameters. Alternative versions of some well-known models such as piecewise exponential, proportional hazards, and software reliability models are shown to be included in our unifying framework. In general, latent parameters of many reliability models are high dimensional, and their inference requires approximating methods such as Markov chain Monte Carlo (MCMC) or Laplace. Latent states in our models are related across stages through a non-Gaussian state-space framework. This feature makes the models mathematically tractable and allows for the exact computation of the marginal likelihood function, despite the non-Gaussianity of the state. Our non-Gaussian evolution models circumvent the need for approximations, which are required in similar likelihood-based approaches. In addition, they allow for reduction of the dimension of the problem. Real-life examples illustrate the approach and indicate advantages over other existing models.