Dependency-aware software release planning

Mougouei, Davoud; Powers, David M. W.; Moeini, Asghar

doi:10.1109/icse-c.2017.74

Cited by 14 publications

(12 citation statements)

References 34 publications

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“…Also, value-annotated APIs can help detect value dependencies [13,[15][16][17][18] and conflicts in software code [19]. This is particularly important for reconciling value conflicts (tensions) in software projects.…”

Section: Value Annotation Of Apismentioning

confidence: 99%

Engineering Human Values in Software through Value Programming

Mougouei

2020

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops

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Ignoring human values in software development may disadvantage users by breaching their values and introducing biases in software. This can be mitigated by informing developers about the value implications of their choices and taking initiatives to account for human values in software. To this end, we propose the notion of Value Programming with three principles: (P1) annotating source code and related artifacts with respect to values; (P2) inspecting source code to detect conditions that lead to biases and value breaches in software, i.e., Value Smells; and (P3) making recommendations to mitigate biases and value breaches. To facilitate value programming, we propose a framework that allows for automated annotation of software code with respect to human values. The proposed framework lays a solid foundation for inspecting human values in code and making recommendations to overcome biases and value breaches in software.

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Section: Value Annotation Of Apismentioning

confidence: 99%

Engineering Human Values in Software through Value Programming

Mougouei

2020

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops

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“…Fuzzy graphs have demonstrated to properly capture imprecision of real world problems [4,6]. Hence, we have used algebraic structure of fuzzy graphs for capturing the imprecision associated with strengths of value-related dependencies.…”

Section: Modeling Dependenciesmentioning

confidence: 99%

“…I i, j > 0 states that e j will ultimately in uence the value of e i in a positive way whereas I i, j < 0 indicates that the ultimate in uence of e j on e i is negative. e extent to which the value of an element e i is in uenced by ignoring positive value-related dependencies and/or selecting negative value-related dependencies of e i , is referred to as the penalty [6] of e i and denoted by p i . p i is calculated by taking supremum over the overall in uences of all ignored positive dependencies and selected negative dependencies of e i as given by (6).…”

mentioning

confidence: 99%

“…e extent to which the value of an element e i is in uenced by ignoring positive value-related dependencies and/or selecting negative value-related dependencies of e i , is referred to as the penalty [6] of e i and denoted by p i . p i is calculated by taking supremum over the overall in uences of all ignored positive dependencies and selected negative dependencies of e i as given by (6). In this equation, n denotes the total number of elements (E : {e 1 , ..., e n }) and x j speci es whether an elements e j is selected (x j = 1) or not (x j = 0).…”

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confidence: 99%

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An Integer Linear Programming Model for Binary Knapsack Problem with Dependent Item Values

Mougouei

Powers

Moeini

2017

AI 2017: Advances in Artificial Intelligence

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Binary Knapsack Problem (BKP) is to select a subset of an element (item) set with the highest value while keeping the total weight within the capacity of the knapsack. is paper presents an integer programming model for a variation of BKP where the value of each element may depend on selecting or ignoring other elements. Strengths of such Value-Related Dependencies are assumed to be imprecise and hard to specify. To capture this imprecision, we have proposed modeling value-related dependencies using fuzzy graphs and their algebraic structure. KEYWORDSBinary Knapsack Problem, Integer Programming, Dependency, Value MODELING DEPENDENCIESFuzzy graphs have demonstrated to properly capture imprecision of real world problems [4,6]. Hence, we have used algebraic structure of fuzzy graphs for capturing the imprecision associated with strengths of value-related dependencies. We have specially modied the classical de nition of fuzzy graphs in order to consider not only the strengths but also the qualities (positive or negative) [5-7] of value-related dependencies (De nition 1).De nition 1. Value Dependency Graph (VDG). A VDG is a signed directed fuzzy graph [11] G = (E, σ, ρ) in which a non-empty set of elements E : {e 1 , ..., e n } constitute the graph nodes. Also, the qualitative function σ : E × E → {+, −, ±} and the membership function ρ : E×E → [0, 1] denote qualities and strengths of explicit value-related dependencies receptively. As such, a pair of elements (e i , e j ) with ρ i, j 0 and σ i, j ± denotes an explicit value-related dependency from e i to e j . It is clear that we have ρ i, j = 0 if the value of an element e i is not explicitly in uenced by selecting or ignoring e j . In such cases we have σ i, j = ± where ± denotes the quality of (e i , e j ) is non-speci ed.De nition 2. Value-Related Dependencies. A value-related dependency in a VDG G = (E, σ, ρ) is de ned as a sequence of elements d i : e(1), ..., e(k) such that for each e(j) in d i , 2 ≤ j ≤ k, we have ρ j−1, j 0. A consecutive pair e(j − 1), e(j) speci es an explicit value-related dependency from e(j − 1) to e(j).

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“…Moreover, the extant release planning models do not take into account the fact that the value of a software requirement may depend on the presence or absence of other requirements in the optimal set. Ignoring such Value Dependencies increases the risk of value loss through ignoring (selecting) requirements that positively (negatively) influence the values of other requirements [13], [9], [14], [15], [16], [17]. It is imperative that value dependencies are not limited to the economic value; they extend to a broader range of human values [18], [3].…”

Section: Introductionmentioning

confidence: 99%

Human Values in Software Release Planning

Mougouei,

Ghose,

Dam

et al. 2021

Preprint

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Software products have become an integral part of human lives, and therefore need to account for human values such as privacy, fairness, and equality. Ignoring human values in software development leads to biases and violations of human values: racial biases in recidivism assessment and facial recognition software are well-known examples of such issues. One of the most critical steps in software development is Software Release Planning (SRP), where decisions are made about the presence or absence of the requirements (features) in the software. Such decisions are primarily guided by the economic value of the requirements, ignoring their impacts on a broader range of human values. That may result in ignoring (selecting) requirements that positively (negatively) impact human values, increasing the risk of value breaches in the software. To address this, we have proposed an Integer Programming approach to considering human values in software release planning. In this regard, an Integer Linear Programming (ILP) model has been proposed, that explicitly accounts for human values in finding an "optimal" subset of the requirements. The ILP model exploits the algebraic structure of fuzzy graphs to capture dependencies and conflicts among the values of the requirements.

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Dependency-aware software release planning

Cited by 14 publications

References 34 publications

Engineering Human Values in Software through Value Programming

Engineering Human Values in Software through Value Programming

An Integer Linear Programming Model for Binary Knapsack Problem with Dependent Item Values

Human Values in Software Release Planning

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